Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.
A novel approach to jointly address localization and classification of breast...IJECEIAES
Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process.
Analysis of contrast concentration for radiological images using cbir frameworkIAEME Publication
This document summarizes a research paper that proposes a framework for analyzing contrast concentration in radiological images using content-based image retrieval (CBIR). The framework uses adaptive complex independent component analysis and a reference region method to calculate intravascular contrast concentration, which is then corrected using pharmacokinetic modeling. CBIR is then used to incorporate the results into an MRI image database for similarity matching and image retrieval. The goal is to more precisely identify contrast agents in prostate cancer diagnosis to help physicians determine the correct treatment.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
IRJET - A Review on Segmentation of Chest RadiographsIRJET Journal
This document reviews and compares various techniques for segmenting anatomical structures from chest radiographs. It begins with an introduction to image segmentation and its importance in medical imaging. It then describes 12 different segmentation methods that have been used for segmenting lungs and other structures from chest radiographs, including active shape models, active appearance models, pixel classification, visual saliency, convolutional neural networks, and others. For each method, it provides details on the algorithm and compares their performance based on accuracy, sensitivity and specificity. In conclusion, it discusses some of the challenges of medical image segmentation and suggests that hybrid approaches combining multiple techniques may be most effective.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Automatic whole-body bone scan image segmentation based on constrained local ...journalBEEI
In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
A novel approach to jointly address localization and classification of breast...IJECEIAES
Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process.
Analysis of contrast concentration for radiological images using cbir frameworkIAEME Publication
This document summarizes a research paper that proposes a framework for analyzing contrast concentration in radiological images using content-based image retrieval (CBIR). The framework uses adaptive complex independent component analysis and a reference region method to calculate intravascular contrast concentration, which is then corrected using pharmacokinetic modeling. CBIR is then used to incorporate the results into an MRI image database for similarity matching and image retrieval. The goal is to more precisely identify contrast agents in prostate cancer diagnosis to help physicians determine the correct treatment.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
IRJET - A Review on Segmentation of Chest RadiographsIRJET Journal
This document reviews and compares various techniques for segmenting anatomical structures from chest radiographs. It begins with an introduction to image segmentation and its importance in medical imaging. It then describes 12 different segmentation methods that have been used for segmenting lungs and other structures from chest radiographs, including active shape models, active appearance models, pixel classification, visual saliency, convolutional neural networks, and others. For each method, it provides details on the algorithm and compares their performance based on accuracy, sensitivity and specificity. In conclusion, it discusses some of the challenges of medical image segmentation and suggests that hybrid approaches combining multiple techniques may be most effective.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Automatic whole-body bone scan image segmentation based on constrained local ...journalBEEI
In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
Brain Tumor Segmentation Based on SFCM using Neural NetworkIRJET Journal
This document describes a proposed system for brain tumor segmentation using neural networks. The system involves 4 phases: 1) Preprocessing MRI images using dual-tree complex wavelet transforms for feature extraction. 2) Spatial fuzzy C-means clustering to classify tissues into normal, tumor core and edema classes. 3) Extracting features using the dual-tree complex wavelet transforms. 4) Classifying the features using a backpropagation neural network to identify normal and abnormal brain tissues. The goal is to automatically and accurately segment brain tumors from MRI images to aid diagnosis and reduce analysis time for radiologists. The system was tested on real patient MRI data and achieved accurate segmentation results.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on Segmentation of Partially Overlapping ObjectsIRJET Journal
This document summarizes several existing methods for segmenting partially overlapping objects in digital images. It discusses challenges in segmenting overlapping objects and different approaches researchers have used, including watershed-based methods, graph cuts algorithms, active shape models, and level set methods. The goal of segmentation is to partition an image into meaningful regions to analyze objects and boundaries. Efficient segmentation of overlapping objects remains an important challenge in image processing.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
Texture Analysis As An Aid In CAD And Computational Logiciosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
4D radiotherapy aims to adapt treatment plans based on organ and tumor motion over time. This requires 4D data management systems to record treatment delivery and portal images over time. Image processing tools like deformable registration and model-based segmentation can help automate identifying organ motion between 3D scans. Adaptive planning approaches could modify plans at intervals of multiple fractions, daily, or intra-fraction to account for changes. Determining if daily replanning is practical requires considering workload, data management, and the incremental clinical benefits versus costs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Improving Hierarchical Decision Approach for Single Image Classification of P...IJECEIAES
The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification.
This document summarizes IGRT techniques for prostate cancer radiation therapy. It discusses the history of using radiation to treat prostate cancer dating back to 1909. It describes advances like 3D conformal radiation therapy and IMRT which allow shaping radiation doses to the target volume. The document outlines the simulation, planning, and contouring process including using fiducial markers and CT/MRI imaging. It discusses dose escalation trials and techniques to reduce organ motion like immobilization devices. Interfractional and intrafractional prostate motion is analyzed from several studies.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
Comparison of Three Segmentation Methods for Breast Ultrasound Images Based o...IJECEIAES
Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal U...journalBEEI
The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
Optimizing Problem of Brain Tumor Detection using Image ProcessingIRJET Journal
This document summarizes several existing methods for detecting brain tumors using magnetic resonance imaging (MRI). It discusses techniques such as image preprocessing, segmentation, feature extraction, and classification methods. Specifically, it reviews 10 different papers that propose various approaches for brain tumor detection, segmentation, and classification. These include using k-means clustering, fuzzy c-means, probabilistic neural networks, support vector machines, genetic algorithms, and sparse representation classification. The goal is to evaluate and compare different existing methods for automated brain tumor detection and analysis using MRI images.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
Brain Tumor Segmentation Based on SFCM using Neural NetworkIRJET Journal
This document describes a proposed system for brain tumor segmentation using neural networks. The system involves 4 phases: 1) Preprocessing MRI images using dual-tree complex wavelet transforms for feature extraction. 2) Spatial fuzzy C-means clustering to classify tissues into normal, tumor core and edema classes. 3) Extracting features using the dual-tree complex wavelet transforms. 4) Classifying the features using a backpropagation neural network to identify normal and abnormal brain tissues. The goal is to automatically and accurately segment brain tumors from MRI images to aid diagnosis and reduce analysis time for radiologists. The system was tested on real patient MRI data and achieved accurate segmentation results.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on Segmentation of Partially Overlapping ObjectsIRJET Journal
This document summarizes several existing methods for segmenting partially overlapping objects in digital images. It discusses challenges in segmenting overlapping objects and different approaches researchers have used, including watershed-based methods, graph cuts algorithms, active shape models, and level set methods. The goal of segmentation is to partition an image into meaningful regions to analyze objects and boundaries. Efficient segmentation of overlapping objects remains an important challenge in image processing.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
Texture Analysis As An Aid In CAD And Computational Logiciosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
4D radiotherapy aims to adapt treatment plans based on organ and tumor motion over time. This requires 4D data management systems to record treatment delivery and portal images over time. Image processing tools like deformable registration and model-based segmentation can help automate identifying organ motion between 3D scans. Adaptive planning approaches could modify plans at intervals of multiple fractions, daily, or intra-fraction to account for changes. Determining if daily replanning is practical requires considering workload, data management, and the incremental clinical benefits versus costs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Improving Hierarchical Decision Approach for Single Image Classification of P...IJECEIAES
The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification.
This document summarizes IGRT techniques for prostate cancer radiation therapy. It discusses the history of using radiation to treat prostate cancer dating back to 1909. It describes advances like 3D conformal radiation therapy and IMRT which allow shaping radiation doses to the target volume. The document outlines the simulation, planning, and contouring process including using fiducial markers and CT/MRI imaging. It discusses dose escalation trials and techniques to reduce organ motion like immobilization devices. Interfractional and intrafractional prostate motion is analyzed from several studies.
Brain tumor classification using artificial neural network on mri imageseSAT Journals
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
Comparison of Three Segmentation Methods for Breast Ultrasound Images Based o...IJECEIAES
Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal U...journalBEEI
The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
An Automated Pelvic Bone Geometrical Feature Measurement Utilities on Ct Scan...IOSR Journals
This document discusses an automated system for measuring geometric features of the pelvic bone from CT scan images. The system uses patch statistical shape models and a multilevel measurement utility to determine pelvic orientation based on image calibration. It aims to help orthopedic surgeons locate damage areas and landmarks more accurately, especially for obese patients where manual palpation is difficult. The system involves experts to analyze statistical values generated from the measurements to inform treatment decisions.
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentatio...IJECEIAES
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.
Enhancing Segmentation Approaches from Fuzzy-MPSO Based Liver Tumor Segmentat...Christo Ananth
This document summarizes several methods for segmenting liver and liver tumors from medical images. It discusses both automatic and semi-automatic methods. For automatic methods, it describes approaches using statistical shape models, sparse shape composition, convolutional neural networks, and extreme randomized trees. For semi-automatic methods, it outlines techniques using level set models, active contours, Bayesian level sets, hidden Markov measure fields, and graph cuts combined with fuzzy c-means clustering. The document provides an overview of key papers on each of these segmentation approaches.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
A new procedure for lung region segmentation from computed tomography imagesIJECEIAES
Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Feat...IOSR Journals
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks of the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted with different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
Bata-Unet: Deep Learning Model for Liver Segmentationsipij
The document presents a new deep learning model called BATA-Unet for liver segmentation. BATA-Unet is based on the Unet architecture but adds batch normalization layers after each convolution layer. The model was tested on two datasets, MICCAI and 3D-IRCAD, achieving Dice scores of 0.97 and 0.96 respectively. This outperforms the authors' previous BATA-Convnet model as well as other state-of-the-art models for liver segmentation. The document provides background on liver segmentation and reviews several related works that use deep learning and other techniques for medical image segmentation.
BATA-UNET: DEEP LEARNING MODEL FOR LIVER SEGMENTATIONsipij
In computer vision, image segmentation is defined as process of a partition of an image in a number of regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning revolution traditional handcrafted features were used for liver segmentation but with deep learning the features are obtained automatically. There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using 3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in we added a batch normalization layer an after each convolution layer in both construction path and expanding path. The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for
3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we compare it with them.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Bone age assessment based on deep learning architecture IJECEIAES
The fast advancement of technology has prompted the creation of automated systems in a variety of sectors, including medicine. One application is an automated bone age evaluation from left-hand X-ray pictures, which assists radiologists and pediatricians in making decisions about the growth status of youngsters. However, one of the most difficult aspects of establishing an automated system is selecting the best approach for producing effective and dependable predictions, especially when working with large amounts of data. As part of this work, we investigate the use of the convolutional neural networks (CNNs) model to classify the age of the bone. The work’s dataset is based on the radiological society of North America (RSNA) dataset. To address this issue, we developed and tested deep learning architecture for autonomous bone assessment, we design a new deep convolution network (DCNN) model. The assessment measures that use in this work are accuracy, recall, precision, and F-score. The proposed model achieves 97% test accuracy for bone age classification.
The Utilization of Physics Parameter to Classify Histopathology Types of Inva...IJECEIAES
Medical imaging process has evolved since 1996 until now. The forming of Computer Aided Diagnostic (CAD) is very helpful to the radiologists to diagnose breast cancer. KNN method is a method to do classification toward the object based on the learning data which the range is nearest to the object. We analysed two types of cancers IDC dan ILC. 10 parameters were observed in 1-10 pixels distance in 145 IDC dan 7 ILC. We found that the Mean of Hm(yd,d) at 1-5 pixeis the only significant parameters that distingguish IDC and ILC. This parameter at 1-5 pixels should be applied in KNN method. This finding need to be tested in diffrerent areas before it will be applied in cancer diagnostic.
Classification of cervical spine fractures using 8 variants EfficientNet with...IJECEIAES
A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.
Quantitative Comparison of Artificial Honey Bee Colony Clustering and Enhance...idescitation
This paper introduces a comparison of two popular
clustering algorithms for breast DCE-MRI segmentation
purpose. Magnetic resonance imaging (MRI) is an advanced
medical imaging technique providing rich information about
the human soft tissue anatomy. The goal of breast magnetic
resonance image segmentation is to accurately identify the
principal mass or lesion structures in these image volumes.
There are many methods that exist to segment the breast
DCE-MR images. One of these, K-means clustering procedure
provides effective solutions in many science and engineering
fields. They are especially popular in the pattern classification
and signal processing areas and can segment the breast DCE-
MRI with high precision. The artificial bee colony (ABC)
algorithm is a new, very simple and robust population based
optimization algorithm that is inspired by the intelligent
behavior of honey bee swarms. This paper compares the
performance of two image segmentation techniques in the
subject of breast DCE-MR image. In the experiments, the
real dynamic contrast enhanced magnetic resonance images
(DCE- MRI) are used. Results show that artificial bee colony
algorithm performs better in terms of segmentation accuracy,
robustness and speed of computation.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
Similar to Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
2. Int J Elec & Comp Eng ISSN: 2088-8708
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert… (Savitha S.K.)
983
research work towards segmentation considering medical images e.g. [7]-[9]. However, complexities
associated with the chest x-ray are quite different compared to other medical images. At present times, there
are only two fixed positions for capturing chest x-ray i.e. posterior-anterior position and lateral position.
Although, these positions offers 90% visualization to maximum clinical problems associated with chest,
but sometime it veils visualization to some critical problems e.g. nodules, masses, lumps, lesions owing to
presence of bones (i.e. rib cases, clavicle bone, etc). Therefore, the bigger challenge in performing
segmentation is to find such occluded objects and extract the targeted object as foreground.
Due to complexity associated with performing segmentation of such inert region of the chest,
majority of the existing research work is found to adopt machine learning techniques or iterative techniques
on the basis of certain predefined information. Although, all these existing techniques are claimed to offer
good segmentation performance, but their computational performance is quite questionable owing to
involvement of higher degree of recursive operation. Therefore, the present manuscript introduces a simple
and cost effective framework that is capable of performing segmentation using progressive approach that has
acted as a better alternative of recursive approach in existing system. The proposed system also introduces an
analytical model to claim the segmentation performance. Section 2 discusses about the existing research
contribution followed by brief outlining of problems associated with existing system in Section 3. Adopted
research methodology of proposed system is briefed in Section 4 followed by elaborated discussion of
algorithm design in Section 5. Discussion of results accomplished in the proposed study is carried out in
Section 6 and finally the concluding remarks is done in Section 7.
2. RELATED WORK
This section briefs about the existing research techniques carried out towards segmentation
techniques in medical images. Researchers have reviewed about existing imaging techniques of chest
radiographs [10] and thereby extend the discussion in the line of chest radiograph explicitly. At present,
there are various ranges of techniques implemented for segmentation. The work carried out by Wang and
Guo [11] have presented a combined implementation of identifying skin boundary, segmenting contour
regions, and refinement of lung region. Most recently, a unique segmentation process of segregating heart
from the lung field was introduced by Dai et al. [12]. The authors have used convolution-based segmentation
technique in order to construct a network that can discretize between the ground truth information and
synthesized mask. Adoption of threshold-based segmentation scheme can be observed in the work of Shi
et al. [13]. The authors have used random walk algorithm in order to segment lung from chest region further
curvature-based technique was utilized to smoothen the contours. A non-conventional technique of vector
quantization has been found to assist in segmentation as well for chest radiographs as seen in the work of
Han et al. [14]. The work carried out by Shen et al. [15] has used a chain coding technique along with
supervised learning algorithm for carrying out lung segmentation focusing on accuracy. Similar direction of
emphasis towards accuracy in segmentation performance was also carried out by Chae et al. [16]. The author
contributed to present a technique for reconstructing region of segmentation thereby enhancing segmentation
performance.
Gill et al. [17] have presented an atlas-based model for carrying out segmentation using affine
transformation scheme. Ngo and Carneiro [18] have presented a deep learning mechanism using level set
method for lung segmentation. The technique presents good optimization towards shape features during
segmentation. Filho et al. [19] have implemented singularity-based technique integrated with region-growing
method and thresholding scheme together to perform lung segmentation. Ruiz et al. [20] have presented a
heart segmentation technique using thresholding based scheme, filtering, and morphological operations.
Farag et al. [21] have introduced a model that implements shape module along with statistical information
about the intensity. The technique also uses a density estimation method using non-parametric approach for
effective lung segmentation. Lassen et al. [22] have adopted a watershed algorithm for achieving better
classification of lung section during transformation process. However, the process involves lack of efficiency
in learning process prior to perform segmentation. Such problems of learning were addressed in the work of
Feulner et al. [23], [24] by using discriminative approach for segmenting lymph node. The authors have
utilized graph cut algorithm for carrying out segmentation. Jirapatnakul et al. [25] have used surface
estimation technique to identify and segment pulmonary masses from chest portion. Lo et al. [26] have used
region growing mechanism along with morphological operation in order to perform segmentation.
The technique also uses fuzzy logic as well as anatomical modeling using semantic features for enhancing the
segmentation performance. Usage of fuzzy theory has also been reported in the work of Zhou et al. [27] for
assisting in segmentation. The technique uses correlation of pixels in order to perform detection.
Fuzzy clustering technique was also reported to be used in the work of Ji et al. [28] for addressing
segmentation problem.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 982 - 991
984
Pu et al. [29], [30] have presented a unique tree-based scheme considering curvature of human
airway for assisting in threshold-based lung segmentation. The scheme also proved that adoption of radial-
basis function can significantly assist in identification of fissures over lung surfaces. Usage of neural network
and wavelets is reported to enhance the segmentation methods as discussed in work of Ceylan et al. [31].
The authors have also used region-growing method for performing segmentation. Shikata et al. [32] have
discussed a tree-based algorithm that works along with Eigen values for constructing terminal portions of the
tissues on lungs. Chen et al. [33] have presented a technique that uses energy function along with kernel for
assisting in segmentation. The complete modeling is carried out considering statistical approach. Adoption of
region growing technique has also been reported in the work carried out by Jiang et al. [34]. Thresholding-
based segmentation scheme was witnessed to offer better system performance during segmentation process
as seen in the work of Liu et al. [35].
There are various literatures towards segmentation problems that has also been considering different
medical case study apart from lungs. Enikov and Anton [36] have used machine learning technique for
segmenting images with cervical spines. Thresholding-based scheme was adopted by Wang et al. [37] for
faster process of medical image segmentation. Jiang et al. [38] have used level set method along with region
growing for performing segmentation of brain images. Incorporation of game theory for improving
segmentation was discussed in work of Zhong and Wu [39]. Integrated implementation of edge as well as
region was reported in the work of Luo et al. [40] where the segmentation performance has been optimized
using swarm intelligence. Benefits of multi-thresholding schemes for segmenting brain images was discussed
by Liu et al. [41]. Study towards automated segmentation is put forward by Mechrez et al. [42] considering
brain images. The authors have used a recursive patch of images in order to perform segmentation. Similar
brain images were also investigated for segmentation by Cong et al. [43] who used field estimation
technique. Akhavan and Faez [44] illustrated the segmentation algorithm for Retinal blood vessel image by
using Fuzzy and medial filter approach and found effective in detection of retinal blood vessels. A research
towards automatic medical image segmentation is found in Seada et al. [45] and named the model as
"ascending aorta". The model outcomes with nealy 95% of accuracy in segmentation. A Doubly truncated K-
mean clustering and Laplace mixture model is presented for image segmentation by Jyothirmayi et al. [46].
In this, different pixelled images were analyzed the for superiority of model in segmentation.
Hence, there are multiple techniques for assisting in segmentation of medical image in literatures.
The next section outlines the problems associated with existing system.
3. PROBLEM IDENTIFICATION
After reviewing the existing system of segmentation, following problems has been identified:
i) usage of thresholding, rule, model based, edge-based, pixel-wise classification, and region-based schemes
are frequently used for performing segmentation. The significant problems of all the adopted methods are its
assumptions being highly heuristic. Hence, they are not much applicable for performing scalable
segmentation performance and can be used only in preliminary stages. This problem is more high for rule-
based techniques. ii) usage of deformable model-based techniques suffers from significant limitations of
higher contrast and prominent occlusion caused by edges of rib cases, higher dependency of reference model,
and scattered solution from multiple internal attributes during segmentation, iii) usage of machine learning
approach also results in higher dependencies on training operation to be carried out on dataset, iv) lower
convergence speed performance, v) majority of the schemes perform segmentation based on pixel related
information that increases accuracy of segmentation at the cost of computational complexity.
Existing machine learning mechanism performs highly iterative operation that increases the execution time of
the segmentation process. Therefore, there is a need of process that performs less iterative operation and
more progressive segmentation steps that could offer a better balance between accuracy and computation
time. We put forward logic that an effective segmentation algorithm always demand a good equilibrium
between computational complexity and accuracy and thereby maintain an optimal scalability performance.
The next section discusses about the adopted research methodology in order to address such problems.
4. PROPOSED METHODOLOGY
The proposed study is an extension of previous work [47] where the emphasis was on incorporating
multi-level of image pre-processing of chest radiograph. This part of the study introduces a simple and yet
novel segmentation policy as showcased in Figure 1. Adopting analytical research methodology,
the proposed system takes the input of a queried image and is capable of extracting horizontal and vertical
pixel information associated with either intensity or projection. Adoption of this strategy ensures mitigating
any form of adverse effect of rotations in the chest radiographs. The proposed system also introduces usage
4. Int J Elec & Comp Eng ISSN: 2088-8708
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert… (Savitha S.K.)
985
of content-based image retrieval technique in order to narrow down the matching images. The narrowness of
the matched image is further boosted with an aid of similarity score between the queried image and dataset.
The outcome of this process results in set of 5 matching images arranged on the basis of their ranks.
The image with 1st rank is considered for further processing where an extraction of feature is carried out
using visual descriptor. Finally, a simple image registration process is implemented that could further
ascertain the mapping of input to target image. In the entire process, accuracy of the matched image is highly
ensured and therefore a contour-based adjustment is further carried out to eliminate any selection of
unnecessary boundary or region of the lung. Applying cubic spline interpolation further make the system
more enhanced enough to perform segmentation. The proposed system is also capable of performing
segmentation as following i) segmenting complete lung region, ii) segmenting apical region, and iii)
segmenting costophrenic region in order to ensure further application of the outcome with respect to clinical
inference of chest radiographs.
Queried
Image
JSRT
dataset
Selected
Analysis
Option
Sum of
intensity
Sum of
Projection
CBIR
Similarity
Score
Query Image and set of matched images with ranks
Feature
extraction
Visual
Descriptor
Image
registration
Contour
Adjustment
Interpolation Segmentation
Option-1
Option-2
Figure 1. Schematic representation of process flow of proposed segmentation
5. ALGORITHM DESIGN
The proposed system emphasizes on the incorporating a precise segmentation process that can
finally assists in disease diagnosis from the given chest radiographs. The mechanism assists in formulating a
better search condition by focusing on the entire process of making the input image ready for segmentation.
The proposed system consists of 5 sequential algorithms in order to perform an effective segmentation
different from existing system i.e. i) Algorithm for processing the input image, ii) Algorithm for content-
based image retrieval, iii) Algorithm for local feature selection, iv) Algorithm for image registration process,
and v) Algorithm for segmentation. This section discusses about all the algorithms in brief.
1. Algorithm for processing the input image
Input: I
Output: H1, H2
Start
1. init I
2. I=r(I)
3. Sr=∑I
4. 4.Sc=∑(I,2)
5. [H1 b1]=hist(Sr max(Sr))
6. [H2 b2]=hist(Sc, max(Sc))
End
The algorithm takes the input I (Input Image) that after processing results in H1 (Horizontal
Projection), H2 (vertical projection). The input image I is digitized followed by applying resizing operation r
(Line-2). The algorithm computes the summation of row wise elements (Line-3) as well as column wise
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 982 - 991
986
elements (Line-4). Histogram is formed considering the row elements corresponding to Sr (Line-5) and
column section consists of all the maximum values of Sc (Line-6). This operation is essential in the proposed
system as it offers the complete segmentation process to be undertaken either on summation of horizontal-
vertical elements or on projection of horizontal-vertical elements. The projection operation assists in
mitigating any form of rotations in the input image using vertical and horizontal orthogonal projection.
This makes the input image free from any forms of rotational effect. The next stage of the algorithm is to
apply content-based image retrieval process as shown below:
2. Algorithm for content-based image retrieval
Input: ϕ
Output: Sro, Sco, β
Start
1. For i=1: ϕ
2. [(H10 b10) (H20 b20)]ϕ(m i), where a=1, 2, 3, 4
3. Sro=∑(a ϕ), a=5
4. Sco=∑((a ϕ), 2)
5. End
6. For n=1:p
7. ix1=b1==p(n), p1(n)=H10(ix1)
8. ix2=b10==p(n), p2(n)=H10(ix2)
9. End
10. For n=1:q
11. ix1=b2==q(n), q1(n)=H1(ix1)
12. ix2=b20==q(n), q2(n)=H20(ix2)
13. End
14. get rw=n/(n+m)
15. For (x y)=1:(n m)
16. pt=pt+√{p1(x).p2(x)}
17. qt=qt+√{q1(x).q2(x)}
18. End
19. β α.qt+(1- α).qt
20. (m_I m_m) ϕ (a ix(1)), where a=5 and 6
21. (Sro Sco)=∑(m_I)
22. obtain (Sco H10 b10 H20 b20) ϕ(a, ix(1)),where a=1-4
End
This algorithm takes the input of ϕ (database) that after processing yields to Sro (sum of horizontal
intensity), Sco (sum of vertical intensity), β (similarity measure). A specific image dataset ϕ is used where
the first and second row element corresponds to horizontal projection H10 and b10 while third and fourth
row elements correspond to vertical projection H20 and b20 respectively (Line-2). The algorithm also
computes the summation of row-wise (Sro) and column-wise (Sco) element respectively (Line-3 and Line-4).
However, the algorithm chooses to consider sum of horizontal and vertical intensity (i.e. Sro and Sco) only
upon selection of summation of horizontal and vertical elements. On the other hand, the algorithm selects
horizontal and vertical projects (i.e. b10, H10 and b20, H20) upon selection of horizontal-vertical projection.
The next part of the algorithm considers similarity coefficient p that computes the common elements between
the projections b1 and b10 (Line-6). Considering all the values of p, the algorithm checks the projection b1 of
input image to be matching with unit value of projection in the database. Only the horizontal projections H10
are considered for further processing i.e. p1 (Line-7 and Line-8).The similar operation is carried out for
vertical projection with similarity coefficient q (Line-10) for calculating q1 and q2 (Line-11 and Line-12).
The algorithm also computes the relative weight rw using mathematical expression shown in Line-14, where
the variable n and m corresponds to length of p and q. The next part of the algorithm calculates a temporary
variables pt (Line-16) and qt (Line-17) in order to compute a final similarity coefficient β using the
expression shown in Line-19. A sorting process in descending order is carried out for the obtained value of
similarity coefficient. A matrix m_I and m_m represents matched image and (ongoing) matching image
respectively (Line-20). Finally, summations of row-wise and column-wise elements are captured (i.e. Sro and
Sco) from matched image m_I (Line-21) followed by acquiring of sum of horizontal intensity (Sro), sum of
vertical intensity (Sco), horizontal projection (b10, H10), and vertical projection (b20, h20) as shown in
Line-22. Therefore, the good availability of projection-based information significantly assists to mitigate any
6. Int J Elec & Comp Eng ISSN: 2088-8708
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert… (Savitha S.K.)
987
forms of misalignment in the image. At the same time, the algorithm of content-based image retrieval results
in good number of most relevant images thereby reducing the effort of search towards
best fit image. The algorithm also makes use of similarity coefficient in order to confirm the
relevancy score of the queried image with that of the dataset. Upon selection of the best matched image,
the image is now ready to be extracted for its feature using the algorithm below:
3. Algorithm for local feature selection
Input: I
Output: im3
Start
1. [(im1 im2) (des1 des2) (loc1 loc2)]=σ(p1 p2)
2. For i=1: des1
3. [v. in]sort(ic(dp))
4. If (v(1)<dR.v(2))
5. m(i)=in(1)
6. Else
7. m(i)=0
8. End
9. im3p1||p2
End
This algorithm is responsible for extracting local features from the matched image obtained from
content-based image retrieval process. The algorithm design is based on visual descriptor of region-based
shape method. The algorithm takes the input of I (processed input image) that after processing yields im3
(image with local feature). The variables p1 and p2 represents I*255 and m_I*255 respectively.
The algorithm implements a function σ for performing the extraction of local features in the form of sets e.g.
(im1 im2) (des1 des2) (loc1 loc2) as shown in Line-1. The algorithm computes the vector of dot product dp
of first descriptor (des1) and transpose of second descriptor (Line-3). This operation is further followed by
sorting operation in order to obtain v and in maps (Line-3). The algorithm then checks of the v(1) is found
more than distance ratio dR multiplied with v(2) than the unit index is assigned to match (Line-5) or else zero
is assigned (Line-7). The distance ratio dR only keep matches in which the ratio of vector angles from the %
nearest to second nearest neighbour is less than dR. Both the images obtained i.e. p1 and p2 as the outcome.
The processed image is further subjected to registration process as shown below:
4. Algorithm for image registration process
Input: I, m_I
Output: Ireg, mw
Start
1. let I1 and I2 be I and m_I
2. [Ireg m2]μ1(I2 I1, ρ)
3. tformμ2(I2 I1, ρ)
4. mwγ(m_m, tform)
End
This algorithm is mainly responsible for performing image registration process which takes the
input of I (processed input image), m_I (Matched image) in order to generate the output of Ireg (registered
image), mw (contour adjusted image). The proposed system implements a function as a configuration
suitable for registering images using multimodal approach. This function formulates an optimizer as well as
metric configuration in order to register the intensity-based image. The variable ρ depicts a set of both
optimizer and a metric as a structure with explicit information about different optimizer properties for
obtaining convergence over a global maximum. The algorithm implements a function μ1 for registering an
image (Line-2) as well as function μ2 for estimating the geometric transformation require to stabilize the
moving image (Line-3). The next step of the algorithm is to perform transformation of matched image m_m
as per the geometric transformation exhibited by tform. Hence, the outcome mw (Line-4) can be considered
as a transformed image that has better adjustment of the contours of the lung regions. The next part of the
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 982 - 991
988
algorithm continues for final step of segmentation process as shown below:
5. Algorithm for identifying upper and lower lung segment
Input: I, mw
Output: u, l
Start
1. yτ(I)
2. bw=λ(mw)
3. [(xs ys)]co(), where co=f(bw)
4. [xo1 yo1]Ω(y, var)
5. 5.( m1 m2){argmin(ys1 ys2) argmax(ys1 ys2)}
6. uI(nr)
7. lI(end-nr)
End
The algorithm considers the input as transformed image mw obtained from the prior algorithm
(Line-2) and uses a function λ for binarizing the image and thereby resultant image of bw is obtained (Line-
2). The next step is to obtain the binarized boundaries co of the processed image bw (Line-3). The system
than extracts the binarized boundaries with respect to explicit rows in order to obtain (xs1 ys1) and (xs2 ys2).
A filtering function τ is invoked on image I (Line-1) followed by applying recursive process Ω (Line-4)
considering different variables var. Finally, the segmented image is generated by conjoining (xs ys) and
(xo yo) points as follows: the algorithm extracts the minimum arguments of (ys1 ys2) and maximum
arguments of (ys1 ys2) in order to form m1 and m2 respectively (Line-5). The algorithm extracts number of
rows nr and number of columns nc from input image I and obtains the upper portion u (Line-6) and lower
portion l (Line-7). A closer look into the algorithm shows that proposed segmentation technique is quite
different from all conventional segmentation technique in following manner viz. i) it is progressive and not
iterative as seen in existing techniques in literature, ii) the segmentation operation is more into optimizing the
detection performance unless existing system that only focuses on detection, iii) the image registration
process followed by contour adjustment further fine-tunes the edges of lung region without even applying
conventional edge-based segmentation process. This causes the algorithm to obtain more accurate
information about lung region and performs faster identification of it. The next section outlines the result
obtained by proposed study.
6. RESULT ANALYSIS
The assessment of the proposed study has been carried out using Japanese Society Radiological
Technology (JSRT) dataset of chest radiograph where the size of the images is 8,192 KB. All the images are
gray-scale images with color depth of 12 bit. This size is quite bigger compared to conventional medical
images.
The study outcome of the proposed system has been compared with the existing segmentation
techniques. There are diverse studies in literatures pertaining to segmentation and hence we choose the one
that is frequently adopted in segmentation i.e. edge based, threshold-based, and region-based schemes.
Table 1 highlights the outcome of comparative analysis of proposed and existing system with respect to
Edge-based, threshold-based, and region-based.
Table 1. Outcome of Comparative Analysis
Performance Factors Existing [47] Proposed
Edge-based Threshold-based Region-based
Execution Time (s) 1.01968 1.0265 4.2841 0.5487
Accuracy (%) 97% 98% 94% 99%
The region-wise segmentation process results in generation of too much redundant information on
the basis of certain pre-defined factor. Therefore, execution time is more compared to others. Edge-based
being the most frequently adopted technique that essentially depends on either histogram or gradient-based
information that increases the accuracy compared to region-based. However, its performance is nearly similar
to threshold-based segmentation technique. The prime reason for enhanced accuracy of proposed system are
i) narrowing of search objects from dataset on the basis of similarity score, ii) usage of region-based shape
8. Int J Elec & Comp Eng ISSN: 2088-8708
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert… (Savitha S.K.)
989
descriptor for further ensuring the correct selection of lung region, iii) image registration process integrated
with contour adjustment to finally identify the lung region followed by spine interpolation, and iv) capability
to identify the apical and costophrenic regions. Moreover usage of features to perform segmentation further
reduces the search time for similar object causing reduction in execution time for proposed system.
7. CONCLUSION
The contribution of existing literatures towards segmentation process in chest x-ray has not yet
addressed the problem associated with the position of capturing the radiograph. The proposed study considers
certain set of problems e.g. i) conventional position of capturing chest x-ray limits the visibility of certain
inert object and may potential acts as impediment in segmentation process, ii) existing segmentation
techniques are more inclined in using iterative operation that offers accuracy at the cost of computational
complexity, iii) more frequently existing techniques e.g. rule based, machine-learning based, threshold-based,
region-based, edge-based doesn’t assist much in addressing the problem of progressive segmentation. A
simple and novel progressive segmentation process is introduced where emphasis was on reduction of
iterations towards segmentation process. The study outcome shows enhanced accuracy and reduced
execution time as compared to frequently used segmentation algorithms.
REFERENCES
[1] Campilho, M. Kamel, “Image Analysis and Recognition: 7th International Conference,” ICIAR 2010, Póvoa de
Varzim, Portugal, June 21-23, 2010, Proceedings”, Springer Science & Business Media, pp. 460, 2010.
[2] David Attwood, Anne Sakdinawat, “X-Rays and Extreme Ultraviolet Radiation: Principles and Applications,”
Cambridge University Press, 2017.
[3] S. Vitulano, “Image: E-learning, Understanding, Information Retrieval and Medical: Proceedings of the First
International Workshop”, Cagliari, Italy, 9-10 June 2003,” World Scientific, 2010.
[4] Christopher Clarke, Anthony Dux, “Chest X-rays for Medical Students,” John Wiley & Sons, 2017.
[5] T. Dohi, I. Sakuma, H. Liao, “Medical Imaging and Augmented Reality: 4th International Workshop Tokyo, Japan,
August 1-2, 2008, Proceedings,” Springer Science & Business Media, pp. 441, 2008.
[6] Allan H. Goroll, Albert G. Mulley, “Primary Care Medicine: Office Evaluation and Management of the Adult
Patient,” Lippincott Williams & Wilkins, 2009.
[7] S. Jaeger, A. Karargyris, S. Candemir, J. Siegelman, L. Folio, S. Antani, and G. Thoma, “Automatic screening for
tuberculosis in chest radiographs: a survey,” Quantitative imaging in medicine and surgery, vol. 3, no. 2,
pp. 89, 2013.
[8] W.S.H.M.W. Ahmad, W. M. Diyana W. Zaki, and M. F. A. Fauzi, “Lung segmentation on standard and mobile
chest radiographs using oriented Gaussian derivatives filter,” Biomedical engineering online, vol. 14, no. 1,
pp. 20, 2015.
[9] P. Campadelli and E. Casiraghi, “Lung field segmentation in digital postero-anterior chest radiographs,” Pattern
Recognition and Image Analysis, pp. 736-745, 2005.
[10] S.K. Savitha and N. C. Naveen, “Study for Assessing the Advancement of Imaging Techniques in Chest
Radiographic Images,” Communications on Applied electronics, vol. 4, pp. 22-34, 2017.
[11] J. Wang and H. Guo, “Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT
Based on Contour Tracing and Correction,” Computational and mathematical methods in medicine, 2016.
[12] W. Dai, J. Doyle, X. Liang, H. Zhang, N. Dong, Y. Li, and E. P. Xing, “SCAN: Structure Correcting Adversarial
Network for Chest X-Rays Organ Segmentation,” ARXIV Preprint ARXIV:1703.08770, 2017.
[13] Z. Shi, J. Ma, M. Zhao, Y. Liu, Y. Feng, M. Zhang, L. He, and K. Suzuki, “Many is Better than One: An
Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images,” Biomed Research
International, 2016.
[14] H. Han, L. Li, F. Han, B. Song, W. Moore and Z. Liang, “Fast and Adaptive Detection of Pulmonary Nodules in
Thoracic CT Images Using a Hierarchical Vector Quantization Scheme,” in IEEE Journal of Biomedical and
Health Informatics, vol. 19, no. 2, pp. 648-659, March 2015.
[15] S. Shen, A. A.T. Bui, J. Cong, and W. Hsu, “An automated lung segmentation approach using bidirectional chain
codes to improve nodule detection accuracy,” Computers in biology and medicine, vol. 57, pp. 139-149, 2015.
[16] S-H. Chae, D. Moon, D. G. Lee, and S.B. Pan, “Medical image segmentation for mobile electronic patient charts
using numerical modeling of IoT,” Journal of Applied Mathematics, pp. 8, 2014.
[17] G. Gill, M. Toews and R.R. Beichel, “Robust initialization of active shape models for lung segmentation in CT
scans: a feature-based atlas approach,” Journal of Biomedical Imaging, pp. 7, 2014.
[18] T. A. Ngo and G. Carneiro, “Lung segmentation in chest radiographs using distance regularized level set and deep-
structured learning and inference,” 2015 IEEE International Conference on Image Processing (ICIP), Quebec City,
QC, 2015, pp. 2140-2143.
[19] P.P. R. Filho, P.C. Cortez, and V. H. C.d. Albuquerque, “3D segmentation and visualization of lung and its
structures using CT images of the thorax,” Journal of Biomedical Science and Engineering, vol. 6, no. 11, pp. 1099,
2013.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 982 - 991
990
[20] J. L-Ruiz, J. M-Sánchez, M. C. B-Jumilla, M. M-Lara, R. V-Monedero, and J. L. S-Gómez, “Automatic image-
based segmentation of the heart from CT scans,” EURASIP Journal on Image and Video Processing, no. 1,
pp. 52, 2014.
[21] A. Farag, H. E. A. E. Munim, J. H. Graham and A. A. Farag, “A Novel Approach for Lung Nodules Segmentation
in Chest CT Using Level Sets,” in IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 5202-5213,
Dec. 2013.
[22] Lassen, E. M. van Rikxoort, M. Schmidt, S. Kerkstra, B. van Ginneken and J. M. Kuhnigk, “Automatic
Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels, and Bronchi,” in IEEE
Transactions on Medical Imaging, vol. 32, no. 2, pp. 210-222, Feb. 2013.
[23] J. Feulner, S. K. Zhou, M. Hammon, J. Hornegger, and D. Comaniciu, “Lymph node detection and segmentation in
chest CT data using discriminative learning and a spatial prior,” Medical image analysis, vol. 17, no. 2,
pp. 254-270, 2013.
[24] J. Feulner, S. K. Zhou, M. Hammon, J. Hornegger, and D. Comaniciu, “Segmentation based features for lymph
node detection from 3-d chest ct,” In International Workshop on Machine Learning in Medical Imaging,Springer,
Berlin, Heidelberg, pp. 91-99, 2011.
[25] A.C. Jirapatnakul, Y.D. Mulman, A.P. Reeves, D. F. Yankelevitz, and C.I. Henschke, “Segmentation of
juxtapleural pulmonary nodules using a robust surface estimate,” Journal of Biomedical Imaging, pp. 15, 2011
[26] P. Lo, J. Goldin, D. Oria, A. Banola, and M. Brown, “Historic automated lung segmentation method: Performance
on LOLA11 data set,” In Proc. 4th Intern. MICCAI Workshop on Pulmonary Image Analysis, Toronto, Canada,
pp. 257-260. 2011.
[27] N. Zhou, T. Yang, and S. Zhang, “An improved FCM medical image segmentation algorithm based on MMTD,”
Computational and mathematical methods in medicine, 2014.
[28] S. Ji, B. Wei, Z. Yu, G. Yang, and Y. Yin, “A new multistage medical segmentation method based on superpixel
and fuzzy clustering,” Computational and mathematical methods in medicine, 2014
[29] J. Pu, C. Fuhrman, W. F. Good, F. C. Sciurba and D. Gur, “A Differential Geometric Approach to Automated
Segmentation of Human Airway Tree,” in IEEE Transactions on Medical Imaging, vol. 30, no. 2, pp. 266-278,
Feb. 2011.
[30] J. Pu et al., “Pulmonary Lobe Segmentation in CT Examinations Using Implicit Surface Fitting,” in IEEE
Transactions on Medical Imaging, vol. 28, no. 12, pp. 1986-1996, Dec. 2009.
[31] M. Ceylan, Y. Ozbay, O.N. Ucan and E. Yildirim, “A novel method for lung segmentation on chest CT images:
complex-valued artificial neural network with complex wavelet transform,” Turkish Journal of Electrical
Engineering & Computer Sciences, vol. 18, no. 4, pp.613-624, 2010.
[32] H. Shikata, G. McLennan, E. A. Hoffman, and M. Sonka, “Segmentation of pulmonary vascular trees from thoracic
3D CT images,” Journal of Biomedical Imaging, pp. 24, 2009.
[33] Chen, Q-H. Zou, W-S. Chen, and Y. Li, “A fast region-based segmentation model with Gaussian kernel of
fractional order,” Advances in Mathematical Physics, 2013.
[34] H. Jiang, B. He, D. Fang, Z. Ma, B. Yang, and L. Zhang, “A region growing vessel segmentation algorithm based
on spectrum information,” Computational and mathematical methods in medicine, 2013.
[35] J. Liu, J. Zheng, Q. Tang, and W. Jin, “Minimum error thresholding segmentation algorithm based on 3d grayscale
histogram,” Mathematical Problems in Engineering, 2014.
[36] E.T. Enikov and R. Anton, “Image segmentation and analysis of flexion-extension radiographs of cervical spines,”
Journal of medical engineering, 2014.
[37] W. Wang, L. Duan, and Y. Wang, “Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of
Distribution Algorithm,” Journal of Electrical and Computer Engineering, 2017.
[38] W. Jiang, Z. Zhou, X. Ding, X. Deng, L. Zou, and B. Li, “Level Set Based Hippocampus Segmentation in MR
Images with Improved Initialization Using Region Growing,” Computational and mathematical methods in
medicine, 2017.
[39] J. Zhong and H. Wu, “Evolutionary Game Algorithm for Image Segmentation,” Journal of Electrical and
Computer Engineering, 2017.
[40] Y. Luo, L. Liu, Q. Huang, and X. Li, “A Novel Segmentation Approach Combining Region-and Edge-Based
Information for Ultrasound Images,” BioMed research international, 2017.
[41] S. Liu, X. Shen, Y. Feng, and H. Chen, “A Novel Histogram Region Merging Based Multithreshold Segmentation
Algorithm for MR Brain Images,” International journal of biomedical imaging, 2017.
[42] R. Mechrez, J. Goldberger, and H. Greenspan, “Patch-based segmentation with spatial consistency: application to
MS lesions in brain MRI,” Journal of Biomedical Imaging, vol.3, 2016.
[43] W. Cong, J. Song, K. Luan, H. Liang, L. Wang, X. Ma, and J. Li, “A Modified Brain MR Image Segmentation and
Bias Field Estimation Model Based on Local and Global Information,” Computational and mathematical methods
in medicine, 2016.
[44] Razieh Akhavan, Karim Faez, “A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation,’
International Journal of Electrical and Computer Engineering (IJECE), vol. 4, no. 4, pp. 561-572, August 2014.
[45] Noha A. Seada, Safwat Hamad, Mostafa G. M. Mostafa, “Model-based Automatic Segmentation of Ascending
Aorta from Multimodality Medical Data,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 6, no. 6, pp. 3161-3173, December 2016.
10. Int J Elec & Comp Eng ISSN: 2088-8708
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert… (Savitha S.K.)
991
[46] T. Jyothirmayi, K. Srinivasa Rao, P. Srinivasa Rao, Ch. Satyanarayana, “Image Segmentation Based on Doubly
Truncated Generalized Laplace Mixture Model and K Means Clustering,” International Journal of Electrical and
Computer Engineering (IJECE), vol. 6, no. 5, pp. 2188-2196, october 2016.
[47] Savitha S. K and N. C. Naveen, “Algorithm for pre-processing chest-x-ray using multi-level enhancement
operation,” 2016 International Conference on Wireless Communications, Signal Processing and Networking
(WiSPNET), Chennai, 2016, pp. 2182-2186.
BIOGRAPHIES OF AUTHORS
Savitha S. K., working as Assistant Professor in Bangalore Institute of Technology, Bengaluru,
Karnataka in Computer Science Department. She has finished her B.E from Bangalore University,
India. She has done her M.Tech from Kuvempu University, India. She is pursuing PhD from VTU,
Belagavi, India. Her Area of Interest is in Digital Image Processing, Database Management
System and Data Mining. She has pubslished 6 papers in national and internation journals and
Conferences. She has around 12 years of academic experience.
Dr. Naveen N. C., working as Professor, Department of Computer Science & Engineering,
JSSATE, Bengaluru, India. He has completed B.E from Bangalore University, India. M.E from
Bangalore University, India. He has completed his PhD from SRM University, Chennai, India. He
has around 16 years of acandemic experience and 3 years of industrial experience. He has
published 12 papers in national and internation journals and conferences.