The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Brea...TELKOMNIKA JOURNAL
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
The document describes a study that used convolutional neural networks (CNNs) to detect brain tumors in MRI images. Three CNN models were developed and their performance was evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrices. Model 3 achieved the highest test accuracy of 94% for tumor detection. In total, over 2000 MRI images were used in the study after data augmentation. The CNN models incorporated convolution, pooling, and fully connected layers to analyze image features and classify tumors. This research demonstrates that CNNs can accurately detect brain tumors in medical images.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
Comparative Assessments of Contrast Enhancement Techniques in Brain MRI Imagesijtsrd
Image processing plays a significant role in the medical field, particularly in medical imaging diagnostics, which is a growing and challenging area. Medical imaging is advantageous in diagnosis and early detection of many harmful diseases. One of such dangerous disease is a brain tumor medical imaging provides proper diagnosis of brain tumor. This paper will have an analysis of fundamental concepts as well as algorithms for brain MRI image processing. We have adhered several image processing steps on brain MRI images, conducting specific contrast enhancements and segmentation techniques, and evaluating every techniques performance in terms of evaluation parameters. The methods evaluated based on two measurement criteria, Peak Signal to Noise Ratio PSNR and Mean Square Error MSE , namely Contrast Stretching, Shock Filter, Histogram Equalization, Contrast Limited Adaptive Histogram Equalization CLAHE . This comparative analysis will be handy in identifying the best way for medical diagnosis, which would be the best method for providing better performance for brain MRI image analysis than others. Anchal Sharma | Mr. Mukesh Kumar Saini "Comparative Assessments of Contrast Enhancement Techniques in Brain MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30336.pdf Paper Url :https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/30336/comparative-assessments-of-contrast-enhancement-techniques-in-brain-mri-images/anchal-sharma
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Brea...TELKOMNIKA JOURNAL
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
The document describes a study that used convolutional neural networks (CNNs) to detect brain tumors in MRI images. Three CNN models were developed and their performance was evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrices. Model 3 achieved the highest test accuracy of 94% for tumor detection. In total, over 2000 MRI images were used in the study after data augmentation. The CNN models incorporated convolution, pooling, and fully connected layers to analyze image features and classify tumors. This research demonstrates that CNNs can accurately detect brain tumors in medical images.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
Comparative Assessments of Contrast Enhancement Techniques in Brain MRI Imagesijtsrd
Image processing plays a significant role in the medical field, particularly in medical imaging diagnostics, which is a growing and challenging area. Medical imaging is advantageous in diagnosis and early detection of many harmful diseases. One of such dangerous disease is a brain tumor medical imaging provides proper diagnosis of brain tumor. This paper will have an analysis of fundamental concepts as well as algorithms for brain MRI image processing. We have adhered several image processing steps on brain MRI images, conducting specific contrast enhancements and segmentation techniques, and evaluating every techniques performance in terms of evaluation parameters. The methods evaluated based on two measurement criteria, Peak Signal to Noise Ratio PSNR and Mean Square Error MSE , namely Contrast Stretching, Shock Filter, Histogram Equalization, Contrast Limited Adaptive Histogram Equalization CLAHE . This comparative analysis will be handy in identifying the best way for medical diagnosis, which would be the best method for providing better performance for brain MRI image analysis than others. Anchal Sharma | Mr. Mukesh Kumar Saini "Comparative Assessments of Contrast Enhancement Techniques in Brain MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30336.pdf Paper Url :https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/30336/comparative-assessments-of-contrast-enhancement-techniques-in-brain-mri-images/anchal-sharma
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation and clustering techniques that have been used for this purpose, including thresholding, edge-based segmentation, region-based segmentation, fuzzy c-means clustering, and k-means clustering. The document also reviews related work applying these methods and evaluates their effectiveness at automatically detecting and segmenting brain tumors from MRI data.
Comparative performance analysis of segmentation techniquesIAEME Publication
This document compares the performance of several image segmentation techniques: global thresholding, adaptive thresholding, region growing, and level set segmentation. It applies these techniques to medical and synthetic images corrupted with noise and evaluates the segmentation results using binary classification metrics like sensitivity, specificity, accuracy, and precision. The results show that level set segmentation best preserves object boundaries, adaptive thresholding captures most image details, and global thresholding has the highest success rate at extracting regions of interest. Overall, the study aims to determine the optimal segmentation method for medical images from CT scans.
Medical Image Segmentation Based on Level Set MethodIOSR Journals
This document presents a new medical image segmentation technique based on the level set method. The technique uses a combination of thresholding, morphological erosion, and a variational level set method. Thresholding is applied to determine object pixels, followed by optional erosion to remove small fragments. Then a variational level set method is applied on the original image to evaluate the contour and segment objects. The technique is tested on various medical images and provides good segmentation results, though it struggles with complex images containing multiple distinct objects.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
The document presents a new approach for automatically classifying normal and abnormal brain MRI images. The proposed method consists of four stages: 1) preprocessing the images to reduce noise, 2) applying discrete multiwavelet transform to reduce dimensionality, 3) extracting texture features using first-order and second-order statistics, and 4) using a probabilistic neural network classifier to classify images as normal or abnormal. The method is also intended to segment and detect suspicious abnormal (tumor) areas to aid diagnosis and reduce computational time for clinicians.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Feature Extraction and Analysis on Xinjiang High Morbidity of Kazak Esophagea...CSCJournals
Image feature extraction technology has been widely applied in image data mining, pattern recognition and classification. Esophageal cancer is a common digestive malignant tumor, China is one of the world's highest incidence and mortality rates of esophageal cancer among the countries, Xinjiang Uygur Autonomous Region is a high incidence area of esophageal cancer, and the kazak is the esophageal cancer high-risk groups. In this paper, we selected 60 advanced esophageal X-ray barium images, half of them are constricted esophagus and the rest are ulcerous esophagus. Firstly, image preprocessing approaches were used to preprocess images. Secondly, extracting the gray-scale histogram features and the GLCM features of the images, then composing the two features into comprehensive feature. Finally, using Bayes discriminant analysis to verify the classification ability of the comprehensive feature. The classification accuracy for constricted esophagus was 86.7%, for ulcerous esophagus was 93.3%.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
CLASSIFICATION OF UPPER AIRWAYS IMAGES FOR ENDOTRACHEAL INTUBATION VERIFICATION csandit
This paper addresses the problem of classification of upper airways images for endotracheal intubation verification in order to improve the safety of patients undergoing general
anaesthesia. The proposed method is based on textural features utilized in a continuous probabilistic framework using parallel Gaussian mixture models (GMMs). The classification
decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 200 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
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.
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
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
Classification of Upper Airways Images for Endotracheal Intubation Verification cscpconf
This document discusses a proposed method for classifying upper airway images to verify proper endotracheal tube placement during intubation. The method uses textural features extracted from the images and represented using Gaussian mixture models in a probabilistic framework. Evaluation on 200 images from 10 patients achieved an overall classification accuracy of 92% between three classes: upper trachea, carina, and esophagus. The method provides efficient and reliable classification of medical images taken at different angles during intubation procedures to help ensure proper tube placement.
Reeb Graph for Automatic 3D CephalometryCSCJournals
The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation and clustering techniques that have been used for this purpose, including thresholding, edge-based segmentation, region-based segmentation, fuzzy c-means clustering, and k-means clustering. The document also reviews related work applying these methods and evaluates their effectiveness at automatically detecting and segmenting brain tumors from MRI data.
Comparative performance analysis of segmentation techniquesIAEME Publication
This document compares the performance of several image segmentation techniques: global thresholding, adaptive thresholding, region growing, and level set segmentation. It applies these techniques to medical and synthetic images corrupted with noise and evaluates the segmentation results using binary classification metrics like sensitivity, specificity, accuracy, and precision. The results show that level set segmentation best preserves object boundaries, adaptive thresholding captures most image details, and global thresholding has the highest success rate at extracting regions of interest. Overall, the study aims to determine the optimal segmentation method for medical images from CT scans.
Medical Image Segmentation Based on Level Set MethodIOSR Journals
This document presents a new medical image segmentation technique based on the level set method. The technique uses a combination of thresholding, morphological erosion, and a variational level set method. Thresholding is applied to determine object pixels, followed by optional erosion to remove small fragments. Then a variational level set method is applied on the original image to evaluate the contour and segment objects. The technique is tested on various medical images and provides good segmentation results, though it struggles with complex images containing multiple distinct objects.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
The document presents a new approach for automatically classifying normal and abnormal brain MRI images. The proposed method consists of four stages: 1) preprocessing the images to reduce noise, 2) applying discrete multiwavelet transform to reduce dimensionality, 3) extracting texture features using first-order and second-order statistics, and 4) using a probabilistic neural network classifier to classify images as normal or abnormal. The method is also intended to segment and detect suspicious abnormal (tumor) areas to aid diagnosis and reduce computational time for clinicians.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Feature Extraction and Analysis on Xinjiang High Morbidity of Kazak Esophagea...CSCJournals
Image feature extraction technology has been widely applied in image data mining, pattern recognition and classification. Esophageal cancer is a common digestive malignant tumor, China is one of the world's highest incidence and mortality rates of esophageal cancer among the countries, Xinjiang Uygur Autonomous Region is a high incidence area of esophageal cancer, and the kazak is the esophageal cancer high-risk groups. In this paper, we selected 60 advanced esophageal X-ray barium images, half of them are constricted esophagus and the rest are ulcerous esophagus. Firstly, image preprocessing approaches were used to preprocess images. Secondly, extracting the gray-scale histogram features and the GLCM features of the images, then composing the two features into comprehensive feature. Finally, using Bayes discriminant analysis to verify the classification ability of the comprehensive feature. The classification accuracy for constricted esophagus was 86.7%, for ulcerous esophagus was 93.3%.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
CLASSIFICATION OF UPPER AIRWAYS IMAGES FOR ENDOTRACHEAL INTUBATION VERIFICATION csandit
This paper addresses the problem of classification of upper airways images for endotracheal intubation verification in order to improve the safety of patients undergoing general
anaesthesia. The proposed method is based on textural features utilized in a continuous probabilistic framework using parallel Gaussian mixture models (GMMs). The classification
decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 200 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
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.
Fuzzy k c-means clustering algorithm for medical imageAlexander Decker
This document summarizes and compares several algorithms used for medical image segmentation, including thresholding, classifiers, Markov random field models, artificial neural networks, atlas-guided approaches, deformable models, and clustering analysis methods like k-means and fuzzy c-means. It provides details on the fuzzy c-means and k-means clustering algorithms, including their process and flowcharts. A new fuzzy k-c-means algorithm is proposed that combines fuzzy c-means and k-means clustering to improve segmentation time. The algorithms are tested on MRI brain images and their results are analyzed and compared based on time, iterations, and accuracy.
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
Classification of Upper Airways Images for Endotracheal Intubation Verification cscpconf
This document discusses a proposed method for classifying upper airway images to verify proper endotracheal tube placement during intubation. The method uses textural features extracted from the images and represented using Gaussian mixture models in a probabilistic framework. Evaluation on 200 images from 10 patients achieved an overall classification accuracy of 92% between three classes: upper trachea, carina, and esophagus. The method provides efficient and reliable classification of medical images taken at different angles during intubation procedures to help ensure proper tube placement.
Reeb Graph for Automatic 3D CephalometryCSCJournals
The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The document describes a new automated method for detecting the optic disc in retinal images. The method uses a line operator designed to capture the circular brightness structure of the optic disc. It evaluates image variation along multiple oriented line segments and locates the disc based on the orientation with maximum variation. The method was tested on a dataset and achieved a 96% success rate in detecting the optic disc.
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
This document presents a novel, fully automatic method for brain tumor segmentation and volume estimation using T1-contrasted and T2 MRI scans. The method involves 5 main steps: 1) preprocessing images using anisotropic diffusion filtering, 2) segmenting tumor regions using k-means clustering, 3) combining segmented regions using logical and morphological operations, 4) applying temporal smoothing for error checking, and 5) measuring the tumor volume. The method was tested on brain volumes from a public dataset and compared to 5 other state-of-the-art algorithms, outperforming them in accuracy and closeness to actual tumor volumes.
Maxillofacial Pathology Detection Using an Extended a Contrario Approach Comb...sipij
This document summarizes a method for detecting maxillofacial pathology in 3D CT medical images using an extended a contrario approach combined with fuzzy logic. The method models samples using the Fisher distribution and applies a Fisher test to detect significant changes between a normal sample and one containing a patient. P-values from three measures are combined using fuzzy logic to provide a decision on pathology with a degree of uncertainty. The method was able to detect pathological areas in a test patient but also regions requiring further investigation, showing performance and leaving room for physician exploration.
Brain tumor visualization for magnetic resonance images using modified shape...IJECEIAES
3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
A Novel Efficient Medical Image Segmentation Methodologyaciijournal
Image segmentation plays a crucial role in many medical applications. The threshold based medical image
segmentation approach is the most common and effective method for medical image segmentation, but it
has some shortcomings such as high complexity, poor real time capability and premature convergence, etc.
To address above issues, an improved evolution strategies is proposed to use for medical image
segmentation, there are 2 populations concurrently during evolution, one focuses on local search in order
to search solutions near optimal solution, and the other population that implemented based on chaotic
theory focuses on global search so as to keep the variety of individuals and jump out from the local
maximum to overcome the problem of premature convergence. The encoding scheme, fitness function, and
evolution operators are also designed. The experimental results validated the effectiveness and efficiency of
the proposed approach.
EDGE DETECTION IN SEGMENTED IMAGES THROUGH MEAN SHIFT ITERATIVE GRADIENT USIN...ijscmcj
In this paper, we propose a new method for edge detection in obtained images from the Mean Shift iterative algorithm. The comparable, proportional and symmetrical images are de?ned and the importance of Ring Theory is explained. A relation of equivalence among proportional images are de?ned for image groups in equivalent classes. The length of the mean shift vector is used in order to quantify the homogeneity of the neighborhoods. This gives a measure of how much uniform are the regions that compose the image. Edge detection is carried out by using the mean shift gradient based on symmetrical images. The difference among the values of gray levels are accentuated or these are decreased to enhance the interest region contours. The chosen images for the experiments were standard images and real images (cerebral hemorrhage images). The obtained results were compared with the Canny detector, and our results showed a good performance as for the edge continuity.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
This document presents a methodology for classifying appendicitis based on ultrasound images using image processing techniques in MATLAB. The methodology involves capturing ultrasound images, preprocessing using median filtering, enhancing with adaptive histogram equalization, segmenting using histogram thresholding, detecting edges with Canny edge detection, and measuring the appendix diameter using Euclidean distance. Canny edge detection was found to be most suitable for defining the region of interest around the appendix. The diameter measurement can help diagnose appendicitis and determine severity.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
This document presents an efficient iris recognition system using wavelet transform that achieves a small template size of 364 bits. The system first segments the iris from eye images and applies normalization to account for variations in scale. It then proposes a method to remove the eyelid and eyelash regions by masking parts of the iris. Next, image enhancement increases contrast before feature extraction using wavelet transform. The document evaluates different combinations of wavelet coefficients and quantization levels to determine the most effective approach. Experimental results demonstrate false accept and reject rates of 0% and 1% respectively.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
This document presents a new approach for segmenting skin lesions in dermoscopic images using a fixed-grid wavelet network (FGWN). The FGWN takes R, G, and B color values as inputs and determines the network structure without training. The image is then segmented and the exact lesion boundary is extracted. Experimental results on 30 images showed the FGWN approach achieved better segmentation accuracy than other methods according to 11 evaluation metrics, extracting lesion boundaries more precisely. In conclusion, the FGWN provides an effective tool for automated skin lesion segmentation in dermoscopy images.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
An approach for cross-modality guided quality enhancement of liver imageIJECEIAES
This paper proposes a novel approach for enhancing the contrast of CT liver images using MRI liver images as a guide. The approach has three steps: 1) transforming the CT and MRI images to the same range, 2) adjusting the CT histogram to match the MRI histogram, and 3) applying adaptive histogram equalization to the CT image using two sigmoid functions. Experimental results show that the proposed approach improves image quality metrics like contrast and brightness preservation compared to traditional techniques. Both subjective and objective assessments indicate the approach enhances image contrast while preserving mean brightness and details.
Similar to Detection of uveal melanoma using fuzzy and neural networks classifiers (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
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This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
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politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
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Detection of uveal melanoma using fuzzy and neural networks classifiers
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 4, August 2020, pp. 2213~2223
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i4.14228 2213
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Detection of uveal melanoma using fuzzy and neural
networks classifiers
Daniel F. Santos, Helbert E. Espitia
Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia
Article Info ABSTRACT
Article history:
Received Sep 28, 2019
Revised Mar 4, 2020
Accepted Mar 27, 2020
The use of image processing is increasingly utilized for disease detection. In
this article, an algorithm is proposed to detect uveal melanoma (UM) which
is a type of intraocular cancer. The proposed method integrates algorithms
related to iris segmentation and proposes a novel algorithm for the detection
of UM from the approach of fuzzy logic and neural networks. The study case
results show 76% correct classification in the fuzzy logic system and
96.04% for the artificial neural networks.
Keywords:
Fuzzy Systems
Image processing
Neural networks
Uveal melanoma
This is an open access article under the CC BY-SA license.
Corresponding Author:
Helbert E. Espitia,
Facultad de Ingeniería,
Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.
Email: heespitiac@udistrital.edu.co
1. INTRODUCTION
The traditional image processing is used to solve problems like quality improvement, restoration,
highlight features, among others. Currently, with the emergence of branches such as fuzzy logic and neural
networks, image processing is also being used to address problems related to diseases. Among them is
the uveal melanoma (UM), a subtype of ocular melanoma (OM) [1] a type of intraocular cancer that arises in
the melanocytes of the coloured part of the eye (iris), circle of muscle tissue (ciliary body) or in the largest
part of the uvea track (choroid) which is located beneath the retina in the back part of the eye. Mostly, ocular
melanomas appear within the choroid and come from the melanocytes, which are the cells of the body that
produce pigment [2]. Early diagnosis and local treatment are crucial since survival rates correlate with
the size of the primary tumour. However, approximately 50% of patients will develop metastases with a
survival rate of 6-12 months from diagnosis [3].
Using modern tools like ultrasonography, fluorescein angiography, and optical coherence
tomography can significantly aid in diagnosis [4, 5], roughly 3 out of 4 people with ocular melanoma survive
for at least 5 years. Survival rates tend to be better for cancer detection in earlier stages than for those in later
stages. When cancer is confined to the eye, the relative survival rate at 5 years is around 80%. For people
with eye melanomas that have spread to distant parts of the body, the 5-year relative survival rate is around
15% [6] and 1-year survival rate is 10% to 15% [7–9].
In related works, a large number of projects associated with the iris have been developed. There are
two main applications: identification of people through unique patterns [10–12] and detection of diseases
where Iridology is found, which is a branch of alternative medicine that is responsible for examining patterns,
2. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 4, August 2020: 2213 - 2223
2214
colours, and other characteristics of the iris to determine the health of patients [13, 14]. For example [15],
where the iris is employed for the detection of Alzheimer.
In [16] is shown how to detect diseases in the iris using the Gabor filter. Among the classified
diseases are corneal oedema, iridotomies, and conjunctivitis. Moreover, in [17], through techniques of
Iridology and the Watershed transform diseases are identified, reaching an accuracy of 87.5% in
the detection of kidney problems. In [18], using unique iris features and advanced encryption standard (AES)
images are encrypted, achieving high levels of security.
As shown in [2, 5] there is no approved therapy to treat metastatic uveal melanoma and considering
that its early detection significantly increases the chances of survival, there was developed a method to
identify UM. The proposed method integrates algorithms used in work related to iris segmentation and
proposes a methodology for UM detection from the fuzzy logic and neural network approach, as well as
present a variation of the algorithm proposed by Wildes in [19]. The variation implements additional logic to
increase the accuracy as well as implementing data structures like a red-black tree and disjoint set with
the union by rank and path compression to reduce the turnaround time.
The shown algorithm employed to detect UM consists of: 1) preprocess images with and without
UM, 2) apply a segmentation algorithm to each of these preprocessed images to find the region of interest
(ROI), 3) transform the ROI to another space that allows an analysis with less noise, 4) obtain descriptors of
the ROIs to generate a training dataset, 5) a fuzzy and a neural network classifier are created based on
the training dataset, and finally in 6) the results of the classifiers are analysed and compared.
2. IMAGE PROCESSING
Humans are very good at detecting patterns but unlike machines, we are not good at processing
large amounts of data, then, if the machine is taught to identify patterns it is possible to automate many
specialized tasks such as disease detection. For the present case study, Hough circular transform, adaptive
binarizations, filters, Hu moments, among others techniques are used to train the classifiers to identify UM, a
brief description of those algorithms is provided.
2.1. Hough circular transform
Hough circular transform (HCT) is an algorithm to search circles in images, this approach is used
because it provides robustness to the presence of noise, occlusion and variation of illumination [20]. This
algorithm has three fundamental phases:
1. Accumulator array computation
2. Center estimation
3. Radius estimation
The HCT is used to transform a set of characteristics of points in the space of the image, (1) to a set
of votes accumulated in the space of the parameters (2). These votes are integer values that are stored in an
array. The position of the arrangement that contains the most votes indicates the presence of a circle. The (1)
represents a circle, where 𝑟 is the radius and (𝑥0, 𝑦0) are the coordinates of the centre of the circle.
𝑟2
= (𝑥 − 𝑥0)2
+ (𝑦 − 𝑦0
)2
(1)
The (2) represents the parametrization of the circle where (𝑥0, 𝑦0) are the coordinates of the centre of
the circle and 𝜃 is the angle of inclination. That equation allows transforming a circle into a rectangle.
𝑥 = 𝑥0 + 𝑟cos(𝜃)
𝑦 = 𝑦0
+ 𝑟sin(𝜃)
(2)
2.2. Adaptive thresholding
Conventional thresholding methods use a threshold for all pixels, while adaptive thresholding (AT)
changes the threshold value dynamically on the image. The AT has shown better results with respect to
the traditional thresholding since the illumination and the shadows change depending on the position of
the image [21]. Within the thresholding methods used in this article are: Gaussian thresholding (represented
by (3)) and mean thresholding. In (4) is depicted an example of a Gaussian window.
ℎ(𝑢, 𝑣) =
1
2𝜋𝜎2
𝑒
−
𝑢2+𝑣2
2𝜎2 (3)
3. TELKOMNIKA Telecommun Comput El Control
Detection of uveal melanoma using fuzzy and neural networks classifiers (Daniel F. Santos)
2215
𝐻 =
1
16
[
1 2 1
2 4 2
1 2 1
] (4)
2.3. Hu moments
The Hu Moments allow to obtain image features used in the classification process. The following
definition is important in this regard.
Definition 1 (Moment of an image): A measure that provides a generic representation of an object, with
simple or complex figures [21].
There are a considerable number of used and well-known moments within which we can include
geometric moments [23], Zernike moments [24], rotational moments [25], and complex moments [26].
The invariants of the moment were introduced by Hu in [23], these are very useful properties that can be
extracted from an image because they are not only independent to position, size and orientation but also to
parallel projection, thus they have been widely used to perform pattern recognition, image registration and
image reconstruction [23, 27–29].
2.4. Invariant moments
A two-dimensional moment of order (𝑝 + 𝑞) is defined by the (5).
𝑚 𝑝𝑞 = ∫
∞
−∞
∫
∞
−∞
(𝑥) 𝑝
(𝑦) 𝑞
𝑓( 𝑥, 𝑦) 𝑑𝑥𝑑𝑦 𝑝, 𝑞 = 0,1,2. .. (5)
If the function 𝑓(𝑥, 𝑦) is defined in parts, the moments of all orders exist and the sequence of moments 𝑚 𝑝𝑞
is only determined by the function 𝑓(𝑥, 𝑦), but those moments in the (5) may not be invariant to translation,
rotation or scale, thus the invariant Hu moments can be calculated using the central moments, which are
defined by (6).
𝜇 𝑝𝑞
= ∫
∞
−∞
∫
∞
−∞
(𝑥 − 𝑥′) 𝑝
(𝑦 − 𝑦′) 𝑞
𝑓( 𝑥, 𝑦) 𝑑𝑥𝑑𝑦 𝑝, 𝑞 = 0,1,2 … (6)
The moments 𝜇 𝑝𝑞 are calculated using the image 𝑓(𝑥, 𝑦), these are equivalent to 𝑚 𝑝𝑞 whose centre has been
moved to the centroid of the image, therefore, the moments are invariant to translation [23]. Where the pixel
𝑓(𝑥′
, 𝑦′
) is the centroid of the image and:
𝑥′ =
𝑚10
𝑚00
, 𝑦′ =
𝑚01
𝑚00
With the use of the normalization of the moments the invariance to scale can be obtained. This moment can
be defined by:
𝑛 𝑝𝑞 =
𝜇 𝑝𝑞
𝜇00
𝛾 , 𝛾 =
𝑝 + 𝑞 + 2
2
, 𝑝 + 𝑞 = 2,3, … (7)
Based on those moments, Hu in [23] presented the seven moment invariants shown below:
ℎ1 = 𝑛20 + 𝑛02
ℎ2 = (𝑛20 − 𝑛02)2
+ 4𝑛11
2
ℎ3 = (𝑛30 − 3𝑛12)2
+ (3𝑛21 − 𝜇03)2
ℎ4 = (𝑛30 + 𝑛12)2
+ (𝑛21 − 𝜇03)2
ℎ5 = (𝑛30 − 3𝑛12)(𝑛30 + 𝑛12)[(𝑛30 + 𝑛12)2
− 3(𝑛21 − 𝑛03)2
]
+(3𝑛21 − 𝑛03)(𝑛21 + 𝑛03)[3(𝑛30 + 𝑛12)2
− (𝑛21 + 𝑛03)2
]
ℎ6 = (𝑛20 − 𝑛02)[(𝑛30 + 𝑛12)2
− (𝑛21 + 𝑛03)2
]
+4𝑛11(𝑛30 + 𝑛12)(𝑛21 + 𝑛03)
ℎ7 = (3𝑛21 − 𝑛03)(𝑛30 + 𝑛12)[(𝑛30 + 𝑛12)2
− 3(𝑛21 − 𝑛03)2
]
−(𝑛30 − 3𝑛12)(𝑛21 + 𝑛03)[3(𝑛30 + 𝑛12)2
− (𝑛21 + 𝑛03)2
]
4. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 4, August 2020: 2213 - 2223
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It is important to mention that the noise generated by different factors, such as the type of camera and file
formatting, can produce errors when calculating moments. In other words, moments can vary with
the geometric transformation of the image [29]. In the case of a scale transformation or rotation of the image,
a rounding of pixel positions or interpolation is generated which causes that at a digital level the invariants of
the moment also change [30].
3. RESEARCH METHOD
This section describes the algorithm (see Figure 1), each sub-section represents each stage as
follows: the first one corresponds to the image processing; next, refers to the detection of the region of
interest; afterwards comes the filtration and transformation of those regions to finally perform the feature
extraction.
Figure 1. Stages for image feature extraction
3.1. Image pre-processing
This is the first phase and it is of vital importance to facilitate the detection of the ROI, in which
enters an image 𝐼(𝑥, 𝑦) whose output is the image 𝑀(𝑥, 𝑦). Figure 2 shows the stages through which
the image passes. Using Figure 3 it is shown how this stage works, this image corresponds to an eye affected
with uveal melanoma [31]. The sequence of algorithms that facilitate the detection of the ROI:
- Grayscale transformation: First, an image with three channels RGB is received and transformed into a
grayscale image; this is mainly due to the fact that a grayscale image is easier to process than an RGB
image (see Figure 4).
- Apply median filter: The image is smoothed by applying a median filter, this will decrease the noise,
helping to make the ROI clearer. The result of this process is shown in Figure 5.
- Apply binarization: With this stage the boundaries are clearly defined, the binarization used was
the Adaptive Mean Thresholding and the Adaptive Gaussian Thresholding. Figure 6 shows the result
obtained in this step.
- Image dilation: Again, a process is done to reduce noise, in this case, pepper type noise, using dilation.
The respective result obtained in this step is shown in Figure 7.
Figure 2. The sequence of algorithms that facilitate the detection of the ROI
5. TELKOMNIKA Telecommun Comput El Control
Detection of uveal melanoma using fuzzy and neural networks classifiers (Daniel F. Santos)
2217
Figure 3. Eye affected with uveal melanoma [31]
Figure 4. Image with UM converted to grayscale Figure 5. Image with UM in grayscale smoothed
using a median filter
Figure 6. Image with UM after applying adaptive
mean thresholding
Figure 7. Dilated image
3.2. Detection of the region of interest
The region of interest is the iris, this is also a crucial stage for the detection of UM since an
inappropriate location of the ROI can result in extraction of characteristics from non-relevant parts,
generating a training with noise and therefore an erroneous classification. Based on the literature of iris
segmentation algorithms, it was decided to use a variation of the wildes algorithm due to its ease of
implementation, computational efficiency and suitable properties.
- Algorithm of ROI detection
Algorithm 1 receives the image of Figure 7, calculates the width and height of the image to make an
estimate of the maximum radius and minimum radius that the iris can have, the minimum is calculated based
on a 𝛿 pre-established. As the function of the Hough transform receives as argument the image and the
radius, this procedure is carried out within an iteration varying the 𝑟𝑎𝑑𝑖𝑢𝑠 from 𝑚𝑖𝑛𝑅𝑎𝑑𝑖𝑢𝑠 to 𝑚𝑎𝑥𝑅𝑎𝑑𝑖𝑢𝑠
and at the same time it is executed twice since two types of binarization are used. Because the algorithm is
executed several times a set is used to store unique ROIs. The set stores the ROIs in a red-black tree that will
allow to access the elements quickly and avoiding duplicates. At the end of this stage, there is a set of
possible regions of interest, as seen in Figure 8.
6. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 4, August 2020: 2213 - 2223
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Algorithm 1. Identify ROI
1: procedure 𝐷𝐸𝑇𝐸𝐶𝑇(𝐼(𝑥, 𝑦))
2: 𝑚𝑎𝑥𝑅𝑎𝑑𝑖𝑢𝑠 ← 𝑚𝑖𝑛(𝑤𝑖𝑑𝑡ℎ(𝐼), ℎ𝑒𝑖𝑔ℎ𝑡(𝐼))
3: 𝑚𝑖𝑛𝑅𝑎𝑑𝑖𝑢𝑠 ← 𝑚𝑎𝑥𝑅𝑎𝑑𝑖𝑢𝑠/𝛿
4: 𝑐𝑖𝑟𝑐𝑙𝑒𝑠 ← { }
5: for 𝑟𝑎𝑑𝑖𝑢𝑠 ← 𝑚𝑖𝑛𝑅𝑎𝑑𝑖𝑢𝑠 𝑡𝑜 𝑚𝑎𝑥𝑅𝑎𝑑𝑖𝑢𝑠 do
6: circles.insert(houghTransform(𝐼,radius))
7: return circles
3.3. Filtering of regions of interest
As can be seen in Figure 8 a large number of regions of interest were detected, some valid and others
not, some with noise or very similar, thus it is necessary a filtering stage. Reducing the number of ROIs reduces
the processing stage. Algorithm 2 discards invalid circles. Since the performance of the algorithm deteriorates
under uncontrolled conditions [19], and there is a large number of similar ROIs, Algorithm 3 was made to join
similar ROI, this last one uses disjoint set with union by rank and path compression that by means of a δ of
distance joins similar ROI. The results of this stage can be seen in Figure 9.
Algorithm 2. Filter (ROI)
1: procedure 𝐹𝐼𝐿𝑇𝐸𝑅(𝑟𝑒𝑔𝑖𝑜𝑛𝑠)
2: 𝑎𝑟𝑒𝑎𝑇𝑜𝑡𝑎𝑙 ←(img.width*img.height)
3: 𝑣𝑎𝑙𝑖𝑑𝑅𝑒𝑔𝑖𝑜𝑛𝑠 ← { }
4: for 𝑟𝑒𝑔𝑖𝑜𝑛 in 𝑟𝑒𝑔𝑖𝑜𝑛𝑠 do
5: 𝑎𝑟𝑒𝑎 ← (𝑟𝑒𝑔𝑖𝑜𝑛. 𝑟)2
∗ 𝜋
6: 𝑟𝑎𝑡𝑖𝑜 ← 𝑎𝑟𝑒𝑎/𝑎𝑟𝑒𝑎𝑇𝑜𝑡𝑎𝑙
7: if 𝑟𝑎𝑡𝑖𝑜 𝑖𝑠 𝑖𝑛 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 [𝑎, 𝑏]then
8: if 𝑟𝑒𝑔𝑖𝑜𝑛 is 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑙𝑦 𝑖𝑛𝑠𝑖𝑑𝑒 then
9: validRegions.insert(region)
10: return 𝑣𝑎𝑙𝑖𝑑𝑅𝑒𝑔𝑖𝑜𝑛𝑠 ⊳ The set with the valid regions
Algorithm 3. Join similar (ROI)
1: procedure 𝐽𝑂𝐼𝑁(𝑟𝑒𝑔𝑖𝑜𝑛𝑠)
2: 𝑈𝑛𝑖𝑜𝑛𝐹𝑖𝑛𝑑 𝑢𝑓(regions.length) ⊳ Instance of Structure Union Find
3: for 𝑖 ← 0 to 𝑟𝑒𝑔𝑖𝑜𝑛𝑠. 𝑙𝑒𝑛𝑔𝑡ℎ do
4: for 𝑗 ← 𝑖 + 1 to 𝑟𝑒𝑔𝑖𝑜𝑛𝑠. 𝑙𝑒𝑛𝑔𝑡ℎ do
5: if 𝑑𝑖𝑠𝑡( 𝑟𝑒𝑔𝑖𝑜𝑛𝑖, 𝑟𝑒𝑔𝑖𝑜𝑛 𝑗) < delta then 𝑢𝑓. 𝑢𝑛𝑖𝑡𝑒(𝑟𝑒𝑔𝑖𝑜𝑛𝑖, 𝑟𝑒𝑔𝑖𝑜𝑛 𝑗)
6: return 𝑢𝑓. 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡_𝑟𝑒𝑔𝑖𝑜𝑛𝑠 ⊳ The set with all differente regions after union process
Figure 8. Possible regions of interest detected using
Algorithm 1, obtained from the dilated images
Figure 9. Regions of Interest filtered using
Algorithms 2 and 3
3.4. Transformation of the region of interest
With the detection of the ROI it is applied a transformation that reduces noise in order to isolate the iris
and to obtain the features. The scheme of Figure 10 was used to carry out the process of unwrapping the iris.
The iris region is transformed into a confined rectangular area, recognizing the boundaries is possible to
apply a transformation from Polar coordinates (𝑟, 𝜃) to Cartesian coordinates (𝑥, 𝑦), according to (8) and (9),
where 𝜃 ∈ [0, 2𝜋], (𝑥 𝑝, 𝑦𝑝) represents the direction of the pupil region which is being transformed and
(𝑥𝑖, 𝑦𝑖) is the new location of that iris element. The results showed in Figures 11 and 12 display the filtered
image and the transformation to Cartesian coordinates.
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𝑥𝑖 = 𝑥 𝑝 + 𝑟 cos (𝜃) (8)
𝑦𝑖 = 𝑦𝑝 + 𝑟 sin (𝜃) (9)
Figure 10. Scheme used for transform the region of interest
Figure 11. Region of interest isolated in Polar coordinates
Figure 12. Isolated region of interest in Cartesian coordinates
3.5. Feature extraction
For the features extraction, the Hu moments were selected due to their properties mentioned in detail
in section 2.4. The extraction was performed in a total of 1622 images that are divided according to Table 1.
With these images, the characteristics were extracted and stored in a comma-separated value (csv) file, which
will be later used to train the classifiers.
Table 1. The number of images for feature extraction
Image Number
Without Uveal Melanoma 1424
With Uveal Melanoma 198
3.6. Fuzzy model
The ranges that differentiate healthy and unhealthy iris sets are established using the data set (csv-file);
with this processing, the membership functions were built in a Mamdani fuzzy system (Figure 13). Considering
the seven Hu moments ℎ1, ℎ2, ℎ3, ℎ4, ℎ5, ℎ6, ℎ7 for each moment it was decided to use triangle functions to
implement the antecedents shown in Figure 13, while Gaussian functions are implemented for the consequent;
this represents a numerical value that identifies if the image is healthy or unhealthy, as shown in Figure 14.
The result of this model can be seen in Table 2 in which it was obtained 76% of correct classification.
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Figure 13. Block diagram of the fuzzy inference system
Figure 14. Fuzzy sets used in the output for healthy or unhealthy classification
3.7. Neural networks
Because the proposed fuzzy system has a success rate of 76%, it was decided to perform a neural
network classifier. Supported by an experimental design; different configurations of neural networks were
implemented to obtain the accuracy of the classifier. The neural networks parameters that are changed can be
seen in Table 2. The inputs of the neural network are the seven Hu moments ℎ1, ℎ2, ℎ3, ℎ4, ℎ5, ℎ6, ℎ7 and
the outputs are two: healthy and unhealthy. Figure 15 shows a sample configuration with 7 inputs, 2 hidden
layers with 3 neurons each one and 2 outputs.
Table 2. Parameters and ranges that varied for the neural network test
Paramater Range
Number Hidden Layers [1, 10]
Number Neurons per layer [1, 10]
Type of network Feed forward, Cascade forward, Fitnet
Figure 15. Sample configuration neural network
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Below are the results with three types of neural networks, each configuration was executed four
times, the results of one of the executions are shown below; finally, statistical measures are shown each
configuration. Figure 16 represents the mean squared error (MSE) for each type of neural network, varying
the number of layers and the number of neurons based on the parameters of Table 2. These graphs and
Table 3 present the conditions in which the UM is most likely to be detected. The configurations with low
MSE outcomes are the ones with less than 6 neurons per layer and less than 5 layers.
(a) (b)
(c)
Figure 16. Neural networks results (a) performance feed forward net,
(b) performance cascade forward net, (c) performance FitNet
Table 3. Results of neural network configurations
Type of Network Results
Feed Forward Net Layers/Neurons 4 6 7 8
4 0.0720 0.0491 0.0757 0.0814
5 0.0606 0.1065 0.07783 0.0732
6 0.0778 0.0756 0.0763 0.0592
8 0.0728 0.0679 0.0696 0.0867
Cascade Forward Net Layers/Neurons 2 4 7 8
4 0.0751 0.0667 0.0828 0.0636
6 0.0717 0.0753 0.0819 0.0824
8 0.0712 0.0786 0.0647 0.0606
9 0.08198 0.0611 0.0495 0.0822
FitNet Layers/Neurons 4 5 6 8
1 0.0783 0.0780 0.0823 0.0629
2 0.0629 0.0760 0.0750 0.0757
4 0.0775 0.0729 0.0467 0.0843
6 0.0756 0.0630 0.0753 0.0728
4. RESULTS AND DISCUSSION
As an experimental result, the accuracy rates of the fuzzy system and the neural network system are
shown. In the confusion matrix of Table 4 are shown the results of the fuzzy classifier, the function that was
used to perform the classification can be seen in the (10) where 𝐹 is the fuzzy system created, 𝐻 is
the function that obtains the values of Hu and 𝐼 is the image to be tested. As can be seen in the confusion
matrix of Table 4, the fuzzy system generates better results with healthy images. The hypothesis of these
results is based on the fact that the entry dataset is larger for healthy iris than for unhealthy iris. Given the last
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table is possible to compute some measures like accuracy, error rate, sensitivity and precision that can give
more insights of the model, the results are 0.76633, 0.23366, 0.88847, and 0.83917, respectively. Regarding
the precision and sensitivity can be shown that the system is prone to detect healthy images. After executing
the different configurations of neural networks, a statistical analysis was performed on four data-sets
corresponding to the results of each configuration; 𝜎𝑖 represents the standard deviation of the results of
the execution number 𝑖. The results obtained are shown in Table 5, where it can be seen that the network that
produces the best results is the feed forward network with a success rate of 96.04%.
𝐶( 𝐼) = {
𝑖𝑓 𝐹(𝐻( 𝐼)) > 0 ℎ𝑒𝑎𝑙𝑡ℎ𝑦
𝑖𝑓 𝐹(𝐻( 𝐼)) < 0 𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦
(10)
Table 4. Confusion matrix using the proposed fuzzy system
True diagnosis
Healthy Unhealthy Total
Healthy 1195 229 1424
Unhealthy 150 48 198
Total 1345 277 1622
Table 5. Standard deviation and MSE
Type of Network 𝜎1 𝑀𝑆𝐸1 𝜎2 𝑀𝑆𝐸2 𝜎3 𝑀𝑆𝐸3 𝜎4 𝑀𝑆𝐸4
Feed Forward 0.0093 0.0491 0.4598 0.0408 0.0080 0.0396 0.0098 0.0442
Cascade Forward 0.0066 0.0495 0.00110 0.0463 0.0095 0.0431 0.0238 0.0466
FitNet 0.0090 0.0467 0.0205 0.0445 0.0107 0.0446 0.0066 0.0432
Finally, judging from the results collected through the different executions, it can be seen that the feed
forward network produces the best accuracy of 96.04% with standard deviation of 0.008 which can be improved
from different approaches such as increasing the size of the data set, incrementing the number of descriptors, e.g
Gabor descriptor or using other techniques, e.g ANFIS, convolutional networks or genetic algorithms.
5. CONCLUSION
The proposed methodology was tested using different configurations, the experimental results show
that fuzzy logic and neural networks classifiers provide a suitable system to detect uveal melanoma,
achieving 76% in the fuzzy classifier and for the neural network classifier which performs better with an
accuracy of 96.04% using a Feed Forward Net. For the segmentation of the ROI it was proposed a new
algorithm built over the principles of Wildes algorithm, this new algorithm was done to improve the success
ratio of well-segmented regions of interest (ROIs) as well as the integration of data structures like disjoint-set
with path and range compression to reduce the processing time. As a result of the pre-processing stage, it was
possible to perform detection of the regions of interest. The stage of transformation and use of data structures
was decisive to reduce the noise in the data set and the processing time for the feature extraction. In order to
improve the performance of the classifiers, it is considered to expand the number of images used, as well as
the integration of more descriptors into the training data set such as Gabor descriptors. In a future work
the methodology and the algorithm proposed could be implemented in smarthphones
ACKNOWLEDGMENT
The authors express gratitude to the Facultad de Ingeniería of the Universidad Distrital Francisco
José de Caldas, and also to the Dr. Paul T. Finger, MD.
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