IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Approach for Study and Analysis of Brain Tumor Using Soft Approachjournal ijrtem
Abstract: As of late, picture preparing is one among quickly developing innovation, rising as a center digging zone and a fascinating subject basically in restorative field. Determination of malady, for example, mind cist, Cancer, Diabetes and so forth is brought out through this innovation. Late studies demonstrate that around 600,000 individuals experience the ill effects of mind cist. From Magnetic reverberation pictures (MRI) , manual restriction and division of cists in mind is blunder inclined and tedious. Picture preparing is exceptionally valuable method to call attention to and remove the suspicious ranges from MRI and CT check therapeutic pictures. With this inspiration in this work, Fuzzy C Means (Potential K-implies) bunching is proposed for MRI cerebrum picture division. Prior to the division the Haralick strategy is advanced for highlight annihilation which will enhance the division exactness. A compelling classifier Support Vector Machines (SVM) is utilized to naturally identify the cist from MRI cerebrum picture. Under boisterous or terrible power standardization conditions this methodology turns out to be more vigorous and deliver better results utilizing high determination pictures. Keywords: Potential K Means, Haralic Feature, Magnetic Resonance Image, Support Vector Machine
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
A review deep learning for medical image segmentation using multi modality fu...Aykut DİKER
This paper reviews deep learning approaches for medical image segmentation using multi-modality fusion. It finds that the number of papers on this topic has increased significantly in recent years, as deep learning methods have achieved superior performance over traditional methods. The paper categorizes fusion strategies as early fusion, where modalities are combined before network processing, and late fusion, where each modality is processed separately before fusion. While early fusion is simpler, late fusion can achieve more accurate results by learning complex relationships between modalities. Overall, the paper aims to provide an overview of deep learning fusion methods for multi-modal medical image segmentation.
An Approach for Study and Analysis of Brain Tumor Using Soft Approachjournal ijrtem
Abstract: As of late, picture preparing is one among quickly developing innovation, rising as a center digging zone and a fascinating subject basically in restorative field. Determination of malady, for example, mind cist, Cancer, Diabetes and so forth is brought out through this innovation. Late studies demonstrate that around 600,000 individuals experience the ill effects of mind cist. From Magnetic reverberation pictures (MRI) , manual restriction and division of cists in mind is blunder inclined and tedious. Picture preparing is exceptionally valuable method to call attention to and remove the suspicious ranges from MRI and CT check therapeutic pictures. With this inspiration in this work, Fuzzy C Means (Potential K-implies) bunching is proposed for MRI cerebrum picture division. Prior to the division the Haralick strategy is advanced for highlight annihilation which will enhance the division exactness. A compelling classifier Support Vector Machines (SVM) is utilized to naturally identify the cist from MRI cerebrum picture. Under boisterous or terrible power standardization conditions this methodology turns out to be more vigorous and deliver better results utilizing high determination pictures. Keywords: Potential K Means, Haralic Feature, Magnetic Resonance Image, Support Vector Machine
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
A review deep learning for medical image segmentation using multi modality fu...Aykut DİKER
This paper reviews deep learning approaches for medical image segmentation using multi-modality fusion. It finds that the number of papers on this topic has increased significantly in recent years, as deep learning methods have achieved superior performance over traditional methods. The paper categorizes fusion strategies as early fusion, where modalities are combined before network processing, and late fusion, where each modality is processed separately before fusion. While early fusion is simpler, late fusion can achieve more accurate results by learning complex relationships between modalities. Overall, the paper aims to provide an overview of deep learning fusion methods for multi-modal medical image segmentation.
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
This document describes a proposed hybrid technique for automatic medical image classification and retrieval using information retrieval, support vector machines, and particle swarm optimization. Key aspects of the proposed approach include extracting low-level visual features from images like color, texture, shape and integrating them with semantic metadata. A content analysis system analyzes image descriptors and assigns semantic labels. Images are indexed and classified during a training phase. The proposed system aims to reduce the semantic gap between low-level features and high-level semantics by combining content-based image retrieval with text-based retrieval and machine learning algorithms.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a new segmentation technique for brain MRI images and compares it to existing techniques. The proposed technique is a two-stage brain extraction algorithm (2D-BEA) that first removes noise and enhances brain boundaries, then uses morphological operations to extract the brain region. It is shown to accurately extract the brain from MRI images. The technique is then compared to other segmentation methods like thresholding, edge detection, fuzzy c-means clustering, and k-means clustering. The results demonstrate that the 2D-BEA technique outperforms these other methods in effectively segmenting the brain region from MRI images.
Classification of MR medical images Based Rough-Fuzzy KMeansIOSRJM
The document summarizes a proposed algorithm for classifying MR medical images using Rough-Fuzzy K-Means (FRKM). It begins with an introduction to the challenges of medical image classification and a literature review of previous techniques. It then provides background on rough set theory, fuzzy set theory, and K-means clustering. The proposed FRKM algorithm is described as using rough set theory for feature selection and dimensionality reduction, followed by a K-means clustering with probabilities assigned based on rough set approximations to classify ambiguous areas. Experimental results show the FRKM approach achieves 94.4% accuracy, higher than other techniques.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
This paper presents a system for detecting and classifying brain tumors in MRI images. Features were extracted from 105 brain images, including mean, standard deviation, and derivative. Two classifiers, SVM and KNN, were tested on the features to classify images as normal or abnormal. The SVM classifier achieved 100% accuracy on the test set, demonstrating the system's ability to successfully separate the two classes.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
Illustration of Medical Image Segmentation based on Clustering Algorithmsrahulmonikasharma
Image segmentation is the most basic and crucial process remembering the true objective to facilitate the characterization and representation of the structure of excitement for medical or basic images. Despite escalated research, segmentation remains a challenging issue because of the differing image content, cluttered objects, occlusion, non-uniform object surface, and different factors. There are numerous calculations and techniques accessible for image segmentation yet at the same time there requirements to build up an efficient, quick technique of medical image segmentation. This paper has focused on K-means and Fuzzy C means clustering algorithm to segment malaria blood samples in more accurate manner.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document describes a novel approach to automated classification of brain tumors using probabilistic neural networks (PNN). It discusses how principal component analysis (PCA) can be used to reduce the dimensionality of magnetic resonance (MR) brain images, and then a PNN can classify the tumors. The proposed method involves using PCA for feature extraction and a PNN for classification. This is intended to provide faster and more accurate classification of brain tumors in MR images than conventional human-based methods.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
Comparative Study on Medical Image Classification TechniquesINFOGAIN PUBLICATION
This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Automatic Brain Tumour Detection Using Symmetry InformationIJERA Editor
Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
Role of serum trace elements magnesium, copper and zinc, level in Libyan pati...iosrjce
IOSR Journal of Biotechnology and Biochemistry (IOSR-JBB) covers studies of the chemical processes in living organisms, structure and function of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules, chemical properties of important biological molecules, like proteins, in particular the chemistry of enzyme-catalyzed reactions, genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction. IOSR-JBB is privileged to focus on a wide range of biotechnology as well as high quality articles on genetic engineering, cell and tissue culture technologies, genetics, microbiology, molecular biology, biochemistry, embryology, cell biology, chemical engineering, bioprocess engineering, information technology, biorobotics.
Green Exercise and Dementia Neil Mapes Feb 2011Neil Mapes
This research project explored the benefits of green exercise, or physical activity in nature, for people living with dementia. Key findings include:
- There is evidence that green exercise can help people with dementia feel well and experience reduced symptoms temporarily. However, larger and more rigorous studies are still needed.
- Interviews and anecdotal reports suggest green exercise helps people with dementia positively reframe their identity and maintain a sense of self.
- Experts surveyed agreed that contact with nature is important for well-being, and that support for green exercise should increase as dementia progresses.
- Priorities for future research identified were learning more about impacts on quality of life, determining most effective types of green exercise, and
MR Image Segmentation of Patients’ Brain Using Disease Specific a priori Know...CSCJournals
Segmentation of high quality brain MR images using a priori knowledge about brain structures enables a more accurate and comprehensive interpretation. Benefits of applying a priori knowledge about the brain structures may also be employed for image segmentation of specific brain and neural patients. Such procedure may be performed to determine the disease stage or monitor its gradual progression over time. However segmenting brain images of patients using general a priori knowledge which corresponds to healthy subjects would result in inaccurate and unreliable interpretation in the regions which are affected by the disease. In this paper, a technique is proposed for extracting a priori knowledge about structural distribution of different brain tissues affected by a specific disease to be applied for accurate segmentation of the patients’ brain images. For this purpose, extracted a priori knowledge is gradually represented as disease specific probability maps throughout an iterative process, and then is utilized in a statistical approach for segmentation of new patients’ images. Experiments conducted on a large set of images acquired from patients with a similar neurodegenerative disease implied success of the proposed technique for representing meaningful a priori knowledge as disease specific probability maps. Promising results obtained also indicated an accurate segmentation of brain MR images of the new patients using the represented a priori knowledge, into three tissue classes of gray matter, white matter, and cerebrospinal fluid. This enables an accurate estimation of tissues’ thickness and volumes and can be counted as a substantial forward step for more reliable monitoring and interpretation of progression in specific brain and neural diseases.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
This document describes a proposed hybrid technique for automatic medical image classification and retrieval using information retrieval, support vector machines, and particle swarm optimization. Key aspects of the proposed approach include extracting low-level visual features from images like color, texture, shape and integrating them with semantic metadata. A content analysis system analyzes image descriptors and assigns semantic labels. Images are indexed and classified during a training phase. The proposed system aims to reduce the semantic gap between low-level features and high-level semantics by combining content-based image retrieval with text-based retrieval and machine learning algorithms.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a new segmentation technique for brain MRI images and compares it to existing techniques. The proposed technique is a two-stage brain extraction algorithm (2D-BEA) that first removes noise and enhances brain boundaries, then uses morphological operations to extract the brain region. It is shown to accurately extract the brain from MRI images. The technique is then compared to other segmentation methods like thresholding, edge detection, fuzzy c-means clustering, and k-means clustering. The results demonstrate that the 2D-BEA technique outperforms these other methods in effectively segmenting the brain region from MRI images.
Classification of MR medical images Based Rough-Fuzzy KMeansIOSRJM
The document summarizes a proposed algorithm for classifying MR medical images using Rough-Fuzzy K-Means (FRKM). It begins with an introduction to the challenges of medical image classification and a literature review of previous techniques. It then provides background on rough set theory, fuzzy set theory, and K-means clustering. The proposed FRKM algorithm is described as using rough set theory for feature selection and dimensionality reduction, followed by a K-means clustering with probabilities assigned based on rough set approximations to classify ambiguous areas. Experimental results show the FRKM approach achieves 94.4% accuracy, higher than other techniques.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
This paper presents a system for detecting and classifying brain tumors in MRI images. Features were extracted from 105 brain images, including mean, standard deviation, and derivative. Two classifiers, SVM and KNN, were tested on the features to classify images as normal or abnormal. The SVM classifier achieved 100% accuracy on the test set, demonstrating the system's ability to successfully separate the two classes.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
Illustration of Medical Image Segmentation based on Clustering Algorithmsrahulmonikasharma
Image segmentation is the most basic and crucial process remembering the true objective to facilitate the characterization and representation of the structure of excitement for medical or basic images. Despite escalated research, segmentation remains a challenging issue because of the differing image content, cluttered objects, occlusion, non-uniform object surface, and different factors. There are numerous calculations and techniques accessible for image segmentation yet at the same time there requirements to build up an efficient, quick technique of medical image segmentation. This paper has focused on K-means and Fuzzy C means clustering algorithm to segment malaria blood samples in more accurate manner.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document describes a novel approach to automated classification of brain tumors using probabilistic neural networks (PNN). It discusses how principal component analysis (PCA) can be used to reduce the dimensionality of magnetic resonance (MR) brain images, and then a PNN can classify the tumors. The proposed method involves using PCA for feature extraction and a PNN for classification. This is intended to provide faster and more accurate classification of brain tumors in MR images than conventional human-based methods.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
Comparative Study on Medical Image Classification TechniquesINFOGAIN PUBLICATION
This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Automatic Brain Tumour Detection Using Symmetry InformationIJERA Editor
Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge.
Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels). Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image
Role of serum trace elements magnesium, copper and zinc, level in Libyan pati...iosrjce
IOSR Journal of Biotechnology and Biochemistry (IOSR-JBB) covers studies of the chemical processes in living organisms, structure and function of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules, chemical properties of important biological molecules, like proteins, in particular the chemistry of enzyme-catalyzed reactions, genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction. IOSR-JBB is privileged to focus on a wide range of biotechnology as well as high quality articles on genetic engineering, cell and tissue culture technologies, genetics, microbiology, molecular biology, biochemistry, embryology, cell biology, chemical engineering, bioprocess engineering, information technology, biorobotics.
Green Exercise and Dementia Neil Mapes Feb 2011Neil Mapes
This research project explored the benefits of green exercise, or physical activity in nature, for people living with dementia. Key findings include:
- There is evidence that green exercise can help people with dementia feel well and experience reduced symptoms temporarily. However, larger and more rigorous studies are still needed.
- Interviews and anecdotal reports suggest green exercise helps people with dementia positively reframe their identity and maintain a sense of self.
- Experts surveyed agreed that contact with nature is important for well-being, and that support for green exercise should increase as dementia progresses.
- Priorities for future research identified were learning more about impacts on quality of life, determining most effective types of green exercise, and
Biochemical and Organoleptic Study of the Mahua Flower and Mahua Flower Wine.iosrjce
IOSR Journal of Biotechnology and Biochemistry (IOSR-JBB) covers studies of the chemical processes in living organisms, structure and function of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules, chemical properties of important biological molecules, like proteins, in particular the chemistry of enzyme-catalyzed reactions, genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction. IOSR-JBB is privileged to focus on a wide range of biotechnology as well as high quality articles on genetic engineering, cell and tissue culture technologies, genetics, microbiology, molecular biology, biochemistry, embryology, cell biology, chemical engineering, bioprocess engineering, information technology, biorobotics.
This document discusses a student's third year design thesis project focused on the Elephant & Castle area in London. The area is undergoing gentrification which is changing the neighborhood drastically, but not necessarily benefiting all. The student wants to explore alternative solutions and deconstruct the gentrification process, providing an architectural language that will stand the test of time. The project incorporates aspects like food, art, business, family and ecological equilibrium, and focuses on providing residents the necessary tools to create their own environment.
Identification of Outliersin Time Series Data via Simulation Studyiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Este poema descreve a vida como um abraço de Deus, uma chance de estar presente e criar um legado positivo através de boas ações e realizações para as gerações futuras, deixando uma "feliz saudade". A vida é vista como um ato de emoção e criação divina.
Este documento describe los componentes principales de un autómata programable: el procesador y la memoria. El procesador ejecuta el programa cíclicamente, leyendo instrucciones, realizando operaciones y actualizando estados de entrada y salida. La memoria almacena el programa de usuario y datos de entrada/salida. El documento explica cómo funcionan estos componentes clave y su organización.
O documento discute a evolução da pesquisa cafeeira no Brasil, propondo a criação de um consórcio entre instituições para sustentar tecnologicamente o agronegócio do café. O consórcio teria uma estrutura de gestão com conselhos e comissões para definir prioridades de pesquisa e aprovar projetos relacionados a desafios como sustentabilidade da cafeicultura de montanha, mão de obra escassa, estresses bióticos e abióticos, qualidade e marketing, e transferência de tecnologia.
El documento describe los principales aspectos del contexto de formación tecnológica virtual en gestión logística con el Servicio Nacional de Aprendizaje (SENA), incluyendo la misión y visión del SENA, los símbolos de la institución, los roles del aprendiz y tutor virtual, los servicios de bienestar al aprendiz, y los sistemas de administración y aprendizaje virtual.
As exportações brasileiras de café totalizaram 4,7 milhões de sacas de 60kg no primeiro bimestre de 2013, gerando receita de US$ 933 milhões. As exportações de arábica diferenciado representaram a maior parte da receita, enquanto as de conillon tiveram queda no volume exportado.
Chocowinity Primary School's mission is to provide a safe environment to prepare diverse students for the 21st century. Their vision is to be a top performing school preferred by parents, students, and educators in Beaufort County. The school has approximately 560 students in pre-K through 4th grade from a rural area, with a free/reduced lunch rate of 75%. Test scores show 79% of students met expected growth in 2014-2015. The school focuses on developing strategic readers, increasing comprehension, and enhancing written responses through interventions like Reading Recovery and supplemental instruction. They promote achievement through a balanced literacy approach and digital programs. The school works to increase home-school connections through various family-focused events.
A produção recorde de café no Brasil e em outros países, aliada a uma demanda fraca devido à crise econômica, levou a um aumento dos estoques e uma queda acentuada nos preços do café. Apesar das tentativas do Brasil de reter oferta, os preços continuaram caindo com a indústria preferindo assumir riscos para manter estoques menores. Uma nova safra grande no Brasil e em outros produtores em 2013 pode levar a uma nova pressão sobre os preços se a demanda não se recuperar.
A talk presented on 12 Dec in the Asia-Pacific International Schools Conference on Making and Design (http://www.ltexpo.com.hk/aisc/portfolio/clifford-choy/)
This document outlines a literacy intervention program called "Catching Them Before They Fall" at Chocowinity Primary School. It discusses five key steps to the program: 1) Strong leadership that holds teachers accountable and provides support. 2) Creating a sense of urgency around early literacy interventions. 3) Analyzing student data from various assessments to identify struggling readers. 4) Implementing an integrated literacy framework school-wide with small group instruction. 5) Building teacher capacity through coaching, mentoring, and professional development to lower turnover. The program aims to identify struggling readers early and provide targeted support through a multi-tiered system of interventions to improve literacy outcomes.
ADV420 MSU Xerox Digital Marketing Strategy Final Projectcarterbastien
This document outlines a digital marketing strategy that focuses on gaining customer insights, building quality relationships, and optimizing for mobile. It recommends taking a "less is more" advertising approach, promoting the brand's positive image, and increasing customer satisfaction. The strategy involves optimizing the website for mobile, using SEO best practices, encouraging social media engagement, blogging to provide solutions and drive traffic, and measuring success through analytics.
Efficient Refining Of Why-Not Questions on Top-K Queriesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This summarizes a document reviewing a Bayesian technique for image classifying registration to perform simultaneous image registration and pixel classification. The technique is well-suited for image pairs with two classes of pixels and different intensity relationships. It can be used for template-based segmentation if the class map is known, or for lesion detection by comparing images if the full model is used. Experiments show the technique improves registration and detection when extra classes like lesions are present, especially with small deformations. The model is defined for two classes but can be extended to more classes, though determining the optimal number of classes would require further research.
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
This document proposes integrating human fingerprint expert knowledge with machine learning algorithms to improve quantitative fingerprint analysis. The researchers will design experiments to extract relevant information from experts and use it to guide statistical models. Their goal is to demonstrate how expert knowledge, represented properly, can enhance fingerprint matching performance compared to current automated approaches alone. In particular, they will apply experts' understanding of important fingerprint regions and ability to focus on specific features to statistical modeling.
Performance Analysis of SVM Classifier for Classification of MRI ImageIRJET Journal
This document discusses using support vector machines (SVM) to classify MRI brain images as normal, benign tumor, or malignant tumor. Key steps include preprocessing images using median and Gaussian filters, extracting features using gray level co-occurrence matrix (GLCM) analysis, and training and testing an SVM classifier on the extracted features to classify new MRI images. The methodology first segments regions of interest in the images using k-means clustering, then extracts GLCM texture features from those regions to train and test the SVM for tumor classification.
An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Feat...IOSR Journals
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks of the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted with different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
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.
An Introduction To Artificial Intelligence And Its Applications In Biomedical...Jill Brown
1) The document discusses the use of artificial intelligence techniques in biomedical engineering and medicine. It focuses on using AI to analyze medical signals and images to assist clinicians.
2) Key applications discussed include using neural networks and expert knowledge as intelligent agents to aid diagnosis, as well as using AI systems to automatically realign MRI images and identify corresponding slices from different scans.
3) The document also outlines the major components of AI, including problem solving, knowledge representation, and perception, and how AI can be applied through intelligent agents, a task manager, and a communication system to integrate different types of medical data and decision-making approaches.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
This document summarizes a research paper on using bilateral symmetry analysis to detect brain tumors from MRI images. It begins by introducing the problem of brain tumor detection and importance of asymmetry analysis. It then describes the proposed algorithm which involves defining a bilateral symmetry axis between the two brain hemispheres and detecting any regions of asymmetry that could indicate a tumor. The algorithm uses edge detection techniques to find the symmetry axis. Performance is evaluated on sample patient data and results show the method can successfully identify tumor locations and sizes. In conclusion, analyzing bilateral symmetry is an effective approach for automated brain tumor detection from MRI images.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
An efficient convolutional neural network-based classifier for an imbalanced ...IAESIJAI
Imbalanced datasets pose a major challenge for the researchers while addressing machine learning tasks. In these types of datasets, samples of different classes are not in equal proportion rather the gap between the numbers of individual class samples is significantly large. Classification models perform better for datasets having equal proportion of data tuples in both the classes. But, in reality, the medical image datasets are skewed and hence are not always suitable for a model to achieve improved classification performance. Therefore, various techniques have been suggested in the literature to overcome this challenge. This paper applies oversampling technique on an imbalanced dataset and focuses on a customized convolutional neural network model that classifies the images into two categories: diseased and non-diseased. Outcome of the proposed model can assist the health experts in the detection of oral cancer. The proposed model exhibits 99% accuracy after data augmentation. Performance metrics such as precision, recall and F1-score values are very close to 1. In addition, statistical test is performed to validate the statistical significance of the model. It has been found that the proposed model is an optimised classifier in terms of number of network layers and number of neurons.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
This document presents a hybrid methodology for classifying segmented images using both unsupervised and supervised classification techniques. The proposed methodology involves first segmenting the image into spectrally homogeneous regions using region growing segmentation. Then, a clustering algorithm is applied to the segmented regions for initial classification. Selected regions are used as training data for a supervised classification algorithm to further categorize the image. The hybrid approach combines the benefits of unsupervised clustering and supervised classification. The methodology is evaluated on natural and aerial images to compare its performance to existing seeded region growing and texture extraction segmentation methods.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Image annotation - Segmentation & AnnotationTaposh Roy
This document discusses image annotation and segmentation. It begins with an overview of different types of image annotation including whole image classification, object detection, and image segmentation. It then covers supervised and unsupervised machine learning paradigms for image annotation, with a focus on supervised learning. Specific supervised annotation techniques for medical images are discussed like mean shift, normalized cuts, and level sets algorithms. Advanced clustering techniques for image segmentation like DBSCAN, HDBSCAN, and topological data analysis are also mentioned.
Similar to Texture Analysis As An Aid In CAD And Computational Logic (20)
An Examination of Effectuation Dimension as Financing Practice of Small and M...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Does Goods and Services Tax (GST) Leads to Indian Economic Development?iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Childhood Factors that influence success in later lifeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emotional Intelligence and Work Performance Relationship: A Study on Sales Pe...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer’s Acceptance of Internet Banking in Dubaiiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study of Employee Satisfaction relating to Job Security & Working Hours amo...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumer Perspectives on Brand Preference: A Choice Based Model Approachiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Student`S Approach towards Social Network Sitesiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Broadcast Management in Nigeria: The systems approach as an imperativeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Retailer’s Perception on Soya Products with Special Reference to T...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study Factors Influence on Organisation Citizenship Behaviour in Corporate ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumers’ Behaviour on Sony Xperia: A Case Study on Bangladeshiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Design of a Balanced Scorecard on Nonprofit Organizations (Study on Yayasan P...iosrjce
1. The document describes a study that designed a balanced scorecard for a nonprofit organization called Yayasan Pembinaan dan Kesembuhan Batin (YPKB) in Malang, Indonesia.
2. The balanced scorecard translated YPKB's vision and mission into strategic objectives across four perspectives: financial, customer, internal processes, and learning and growth.
3. Key strategic objectives included donation growth, budget effectiveness, customer satisfaction, reputation, service quality, innovation, and employee development. Customers perspective had the highest weighting, suggesting a focus on public service over financial growth.
Public Sector Reforms and Outsourcing Services in Nigeria: An Empirical Evalu...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Media Innovations and its Impact on Brand awareness & Considerationiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer experience in supermarkets and hypermarkets – A comparative studyiosrjce
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- The study provides insights for retailers on key determinants of customer experience in each format to help them improve strategies and competitive positioning.
Social Media and Small Businesses: A Combinational Strategic Approach under t...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Secretarial Performance and the Gender Question (A Study of Selected Tertiary...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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Organizational Conflicts Management In Selected Organizaions In Lagos State, ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
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It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
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
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
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-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
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- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Texture Analysis As An Aid In CAD And Computational Logic
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. V (May – Jun. 2015), PP 01-06
www.iosrjournals.org
DOI: 10.9790/0661-17350106 www.iosrjournals.org 1 | Page
Texture Analysis As An Aid In CAD And Computational Logic
Charu Sharma
Abstract: The thought to utilize composition examination in restorative imaging has been considered following
the mid-1970s. Then again, the energizing advancement of both surface investigation calculations and PC
innovation restored scientists' enthusiasm for applications for restorative imaging recently. Specialized
advancements in medicinal cross-sectional imaging have opened up new vistas for the investigation of the
human body, empowering both high spatial and temporal resolution. On Difficulties Caused by Missing
Reference Image Datasets, one of the fundamental issues in the improvement of frameworks for the investigation
of restorative pictures or division calculations is the situation of pictures that is utilized for both the
advancement and the testing of the framework. This paper proposes a symptomatic plan which takes after a well
known two-stage approach. At first, textural data is removed from the picture. Accordingly, the extricated data
is nourished into a decisional calculation that is intended to perform the analytic task.The talk covers new
routines for example, acknowledgment and PC helped determination in the field of MRI.Utilitarian MRI for
human cerebrum mapping, treatment control via programmed injury recognition in numerous sclerosis, and
new ways to deal with the diagnosis ofbreast cancer in MRI mammography. In the light of such creative systems
for example acknowledgment in biomedicine, the expanding interest for openly available approval stages is
underlined. With picture surface examination in the part of the visual perceptional capacity, the procedure of
highlight extraction and picture coding is accomplished. The separated elements can now be converted into a
finding by utilizing a decision making calculation, with decisions that range from the standard based models to
the customary factual examination to the more prominent and frequently more effective computerized reasoning
strategies, for example, neural systems and hereditary calculations. By utilizing picture texture analysis as the
preprocessing stride in CAD conspires, the information era procedure is mechanized and, hence, is
reproducible and vigorous. A few methods represent texture on the basis of the spectral properties of an image.
Others are model-based strategies that investigate surface by distinguishing a proper model that mirrors the
former convictions and information about the kind of pictures to be investigated. There are textural components
that depict nearby picture measurements and others that portray worldwide insights.
I. Introduction
Pattern recognition is "the demonstration of taking in crude information and making a move in view of
the class of the pattern". Most research in pattern acknowledgment is about techniques for regulated learning
and unsupervised learning. Pattern recognition plans to characterize information constructed either in light of the
earlier learning or on measurable data removed from the examples. The examples to be characterized are
typically gatherings of estimations or perceptions, characterizing focuses in a suitable multidimensional space.
This is as opposed to pattern coordinating, where the pattern is unbendingly indicated. A complete pattern
recognition framework comprises of a sensor that assembles the perceptions to be arranged or depicted, an
element extraction instrument that figures numeric or typical data from the perceptions, and a depiction strategy
that does the genuine occupation of characterizing or portraying perceptions, depending on the removed
elements.
The characterization or depiction plan is generally takes into account the accessibility of an
arrangement of patterns that have, as of now been grouped or depicted. This arrangement of patterns is termed
the preparation set or the training set, and the subsequent learning methodology is portrayed as managed
learning or in other terms, supervised learning. Learning can likewise be unsupervised in a manner, where the
system is not attached with earlier naming of examples, rather it itself sets up the classes in view of the inherent
regularities of the examples. The grouping or portrayal plan for the most part uses one of the accompanying
methodologies: factual or syntactic. Measurable pattern recognition is in light of factual portrayals of patterns,
accepting that the patterns are created by a probabilistic framework. Grammatical or basicpattern recognition is
in view of the auxiliary interrelationships of elements.
An extensive variety of calculations can be requisitioned for pattern recognition, from basic guileless
Bayes classifiers and neural systems to the intense k-closest neighbor calculation decisional algorithms. Design
acknowledgment is more perplexing when layouts are utilized to produce variations. To emphasize, in English,
sentences regularly take after the "N-VP" (thing - verb expression) design, however some learning of the
English dialect is obliged to identify the pattern. Pattern recognition is considered in numerous fields, including
brain science, ethology, intellectual science and software engineering. Holographic affiliated memory is another
2. Texture analysis as an aid in CAD and computational logic
DOI: 10.9790/0661-17350106 www.iosrjournals.org 2 | Page
sort of example coordinating where an extensive arrangement of scholarly examples in view of psychological
meta-weight is traversed for a little arrangement of target examples.
II. Specific Uses
Figure 1: Faces recognized distinctly among other elements.
The face was consequently recognized by unique programming set. In case of medicinal science,
pattern recognition is the premise for computer aided diagnostic (CAD) frameworks. CAD portrays a
methodology that backs up a specialist's translations and discoveries. Some specific applications are
programmed speech recognition software, characterization of content into a few classifications (e.g. spam/non-
spam email messages), the programmed acknowledgment of written by hand postal codes on postal envelopes,
or the programmed acknowledgment of pictures of human appearances. The last two cases shape the subtopic
picture examination of pattern recognition that makes arrangements with computerized pictures as information
to pattern recognition frameworks.
III. Role Of Image Texture Analysis
The concept of ‘computer aided diagnosis’ (which is also called CAD) refers to software that analyses
a radiographic finding to estimate the likelihood that the feature represents a specific disease process (e.g.
benign versus malignant). To my knowledge, this technology has not yet been approved for clinical use,
specifically depending more on biopsy tests done manually on sample tissues which usually is a very tedious
task. Currently, methods of image texture analysis are undergoing great development and utilization within the
field of medical imaging. Given the general interest and striking growth in computer-aided diagnosis (CAD), the
application of texture analysis in the diagnostic interpretation of radiologic images has become a rapidly
expanding field of research.
Proposed diagnostic scheme follows a familiar two-step approach. Initially, textural information is
extracted from the image. Subsequently, the extracted information is fed into a decisional algorithm (eg. an
artificial neural network) that is designed to perform the diagnostic task. However, from a methodological point
of view, the main attraction of this study is that the combination of image texture analysis and automated
decision making offers a promising approach to a clinical challenge. Successful applications of the above CAD
strategy have been reported for other types of medical images. While further development and testing is required
to establish the true clinical effect of such decisionsupport systems, texture analysis appears to open a new
exciting path in the journey toward CAD in radiology. Consequently, two questions are raised: What is the
realistic contribution of texture analysis in the computer-aided interpretation of medical images, and to what
extent can it be expected to improve interpretative accuracy?
3. Texture analysis as an aid in CAD and computational logic
DOI: 10.9790/0661-17350106 www.iosrjournals.org 3 | Page
IV. Formulation Of The Diagnosis
The symptomatic translation of therapeutic pictures is a multifaceted assignment. Its goal is the exact
recognition and exact portrayal of potential variations from the norm, animportant stride toward the foundation
of powerful treatment. Accomplishing this objective depends on the radiologists' effective mix of two particular
procedures: (a) the procedure of picture observation to perceive special picture patterns and (b) the procedure of
thinking to recognize connections between observed patterns and conceivable conclusions. Both procedures
depend intensely on the radiologists' exact learning, memory, instinct, and tirelessness.
Certainly, the radiologists approach the analytic undertaking with a level of insight, adaptability, and
an ability to think that is hard to copy with a computer brain. In spite of many years of concentrated research in
biological visual frameworks and perceptual insight, there is a constrained comprehension and a continuous
level headed discussion about the fundamental components that underlie visual observation. Interestingly, there
is general understanding that composition is a rich wellspring of visual data and is a key part in image
investigation and perception in people. Textureimportantly provides pieces of information about picturesque
profundity and texture introduction and, in that capacity, depicts the substance of both naturally produced and
manufactured images. Likewise, there is confirmation of perceptual adaption in composition coding systems and
in textural separation. In light of this, specialists have centered their consideration on creating calculations that
can evaluate the textural properties of a picture. Exact proof and analysts' innovativeness have prompted various
calculations for textureevaluation. The accessible calculations normally contrast in the sort of picture data that is
caught and in the coding instrument. The development and differences of accessible systems for surface
examination are a demonstration of the headway of this field.
A broad review of textural definitions, models, and diagnostic calculations can be discovered
somewhere else. Composition examination is at last concerned with robotized techniques that can get image
data from an absolutely computational perspective. As being what is indicated, it is just another kind of numeric
control of advanced or digitized pictures to get quantitative estimations. In any case, in opposition to the
segregation of morphologic data, there is proof that the human visual framework experiences issues in the
separation of textural data that is identified with higher-request insights or ghostly properties on a picture.
Hence, texture investigation can possibly increase the visual aptitudes of the radiologist by taking into
4. Texture analysis as an aid in CAD and computational logic
DOI: 10.9790/0661-17350106 www.iosrjournals.org 4 | Page
consideration the features of the image that may be applicable to the indicative issue however that are not so
much outwardly extractable.
In any case, surface examination is not a panacea for the symptomatic understanding of radiologic
pictures. The quest for composition investigation is taking into account the theory that the surface mark of a
picture is applicable to the indicative issue close by. Moreover, the adequacy of surface investigation is bound
by the kind of calculation that is utilized to concentrate significant components. A choice calculation that uses
consequently separated components has a superior possibility of creating a powerful CAD framework. A CAD
apparatus that uses naturally separated elements addresses a built up clinical shortcoming of the demonstrative
procedure furthermore supplements the radiologists' smart capacities. Texture investigation is appropriately
suited to this issue as the radiologists themselves depend on visual composition to distinguish and portray breast
injuries. The way image handling is utilized as a part of the study can be effortlessly performed with existing
programming bundles significantly streamlining the fuse of the proposed CAD device into the center. The
adequacy of any CAD instrument will dependably be contingent on two things: (a) how well the chosen
components portray the infection that need to be separated and (b) how well the study groupreflects the general
target patient population for the CAD apparatus.
V. Texture Analysis’ Clinical Implementation Along With Cad
Computer aided diagnostic (CAD) is an innovation intended to reduce observational oversights and in
this way decline the false negative rates of doctors deciphering restorative pictures. Planned clinical studies have
exhibited an increment in breast malignancy identification with CAD help. This review quickly depicts the
measurements that have been utilized to characterize CAD framework execution. PC projects have been created
and sanction for utilization in clinical practice that guide radiologists in identifying potential variations from the
norm on indicative radiology exams. This application has been termed PC aided location, usually alluded to as
CAD. The term 'computer aided diagnostic’ alludes to pattern recognition programming that recognizes
suspicious components on the picture and conveys them to the consideration of the radiologist, keeping in mind
the end goal to decline false negative readings.
As presently utilized, the radiologist first audits the exam, then initiates the CAD programming and re-
assesses the CAD-stamped regions of concern before issuing the last report. CAD is as of now FDA and CE
sanctioned for utilization with both film and advanced mammography, for both screening and analytic exams;
for midsection CT; and, for midsection radiographs. The essential objective of CAD is to build the discovery of
illness by diminishing the false negative rate because of observational oversights. The utilization of a PC as
opposed to a second human eyewitness has the benefit of not expanding the requests on the radiologist or
prepared spectator pool. An imperative part of either approach is to expand sickness recognition without a fix
effect on the review and work up rates.
At last, in a few applications CAD, with its related robotized programming instruments, can possibly
give work process efficiencies. CAD calculations are produced to scan for the same components that a
radiologist searches for amid case audit. Hence, for breast growth on mammograms, the CAD calculations hunt
down small scale calcifications and masses both hypothesized and non-theorized, design contortions and
asymmetries. On midsection radiographs and CT filters, flow CAD applications look for aspiratory densities
that have particular physical qualities, e.g. sphericity, that may speak to lung knobs. As anyone might expect,
CAD calculations will underline features that meet the calculation necessities, however which don't speak to
discoveries that the radiologist considers to warrant further examination, i.e. false CAD marks. Likewise, on
occasion a genuine positive CAD mark, upon survey by the radiologist, is released as not justifying further
examination. In this case, the false negative report would be the aftereffect of an interpretive—as opposed to a
discernment.
VI. Cad Methodology: Dealing With Textural Analysis
CAD is in a far-reaching way taking into account profoundly complex pattern recognition. X-ray
pictures are filtered for suspicious structures. Regularly a couple of thousand pictures are obliged to enhance the
calculation. Advanced picture information are replicated to a CAD server in a DICOM-format and are arranged
and investigated in a few stages.
6.1.1 Preprocessing for
Decrease of relics (bugs in pictures)
Picture commotion diminishment
Leveling (harmonization) of picture quality for clearing the picture's distinctive fundamental conditions e.g.
diverse presentation parameter.
5. Texture analysis as an aid in CAD and computational logic
DOI: 10.9790/0661-17350106 www.iosrjournals.org 5 | Page
6.1.2 Segmentation for
Separation of distinctive structures in the picture, e.g. heart, lung, ribcage , conceivable round
Sores
Coordinating with anatomic databank
6.1.3 Structure/ROI (Region of Interest) Analyze
Each distinguished area is examined exclusively for uncommon attributes:
Closeness
Frame, size and area
Reference to near to structures/ ROIs
Normal greylevel worth break down inside of a ROI
Extent of greylevels to outskirt of the structure inside the ROI
6.1.4 Evaluation/ order
After the structure is broke down, every ROI is assessed exclusively (scoring) for the likelihood of a TP.
Subsequently the methodology are:
Nearest-Neighbor Rule
Minimum distance classifier
Cascade Classifier
Bayesian Classifier
Multilayer perception
Radial basis function network (RBF)
SVM
In the event that the recognized structures have come to a certain edge level, they are highlighted in the
picture for the radiologist. Contingent upon the CAD framework these markings can be forever or provisional
spared. The latter's preference is that just the markings which are endorsed by the radiologist are spared. False
hits ought not to be spared, on the grounds that an examination at a later date turns out to be more troublesome
then.
VII. Implementation Of Cad In Accordance With Background Evaluation
The CAD calculations oblige an advanced information set of the picture for examination. In the event
that the picture is obtained on x-ray film, for example, a screen mammogram, the simple picture should first be
digitized. On the other hand, the CAD calculations can specifically investigate pictures obtained in
computerized arrangement, for example, with advanced mammography (FFDM) and CT.
In present practice (and as needed by the FDA), the exam ought to first be investigated and deciphered in the
typical manner. At exactly that point are the CAD imprints shown, taking after which the radiologist re-surveys
those ranges that are provoked by the CAD framework. Two vital standards must be held fast to:
Current CAD frameworks don't check every noteworthy finding. Consequently, the nonappearance of a
CAD check on a discovering the radiologist was worried about on his/her pre CAD audit should not hinder
further assessment.
Current CAD frameworks create numerous more false CAD marks than genuine CAD marks. Thus, it is the
obligation of the radiologist to figure out whether a CAD imprint warrants further assessment.
A CAD framework can be assessed in a few ways, which incorporates examination of information
produced in a research center or test setting, and by the effect of CAD on radiologist execution in a real clinical
work on setting. ‘Standalone' affectability and specificity, this data can be acquired by watching the execution of
a CAD framework on an arrangement of "truth" cases. The fact of the matter is by and large settled by
histological confirmation of the vicinity (e.g. disease) or nonattendance (e.g. clinical postliminary) of malady.
Affectability is dictated by the rate of positive cases in which the CAD framework puts an imprint on
the malady area. The quantity of false CAD marks per typical picture or case is generally utilized as a surrogate
for specificity. The aftereffects of this activity are, obviously, subject to the case accumulation. Inclination,
planned or not, in gathering positive cases that have more obvious discoveries will evidently bring about better
CAD execution analyzed than cases that are less prominent, despite the fact that the CAD calculation is the
same. Along these lines, the same CAD calculation will exhibit changing sensitivities and specificities (false
checks per case) contingent upon the case creation.
A favored technique to contrast CAD frameworks is to determine the sensitivity and false marker rates
on the same arrangement of "truth" cases. These cases must be "obscure" to the CAD framework, that is, they
6. Texture analysis as an aid in CAD and computational logic
DOI: 10.9790/0661-17350106 www.iosrjournals.org 6 | Page
ought not to have been utilized to prepare the CAD calculations. Adequate and regularly extensive quantities of
cases will be required so as to build up measurable noteworthiness of predominance or proportionality in
execution when contrasting CAD frameworks. "Research center" investigations of potential recognition change
initiate radiologists (or other 'pursuers') to assess an arrangement of "truth" cases to focus the affectability and
get back to rate of the unaided pursuer (pre CAD) with that of the pursuer with CAD help. Such studies are
valuable to survey the potential advantage of CAD and give evaluations of expected changes in ailment
discovery and workup/review rates. In any case, the test setting regularly bargains the execution of the pursuer,
in that the pursuers might either, over or under call the investigated cases in a test domain.
This should be possible in a 'consecutive read' clinical trial, in which the exam is first read before, and
later on taking after, CAD information. The adjustment in sickness recognition because of the CAD data, and
additionally the adjustment in the review/workup rates, will focus the commitment of CAD to patient
administration. Vitally, the rate increment in ailment discovery ought to be concordant with, or not exactly, the
rate increment in the review/workup rate.
VIII. Conclusion
The thought to utilize pattern recognition in restorative imaging has been considered following the mid
1970s. On Difficulties Caused by Missing Reference Image Datasets One of the fundamental issues in the
improvement of frameworks for the examination of medicinal pictures or division calculations is the situation of
pictures that is utilized for both the advancement and the testing of the framework. This paper depicts the system
with picture composition examination in the part of the visual perceptional capacity, the procedure of highlight
extraction and picture coding is accomplished. The extricated elements can now be converted into a conclusion
by utilizing a decisional calculation, with decisions that range from the tenet based models to the customary
factual examination to the more prevalent and regularly more fruitful, manmade brainpower systems, for
example, neural systems and genetic calculations.
By utilizing image texture analysis as the preprocessing stride in CAD schemes, the input generation
procedure is computerized and, accordingly, is reproducible and strong. Hitherto, discoveries have demonstrated
that CAD can improve the symptomatic execution of radiologists, in the event that it is utilized as a second
assessment. Be that as it may, it is still hard to foresee its acknowledgement in medicinal analysis, and its
acknowledgement won't be resolved until discoveries from broad clinical utilization exhibit quantifiable
advantages. In the meantime, business CAD items will need to face new difficulties identified with promoting,
specialized bolster, adjustments, and item life-cycles and practices in the human services environment are
always re-imagined.
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