Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images
Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and int...
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MDPI AG
2023-12-01
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| author | Christodoss Prasanna Ranjith Krishnamoorthy Natarajan Sindhu Madhuri Mahesh Thylore Ramakrishna Chandrasekhar Rohith Bhat Vinoth Kumar Venkatesan |
| author_facet | Christodoss Prasanna Ranjith Krishnamoorthy Natarajan Sindhu Madhuri Mahesh Thylore Ramakrishna Chandrasekhar Rohith Bhat Vinoth Kumar Venkatesan |
| author_sort | Christodoss Prasanna Ranjith |
| collection | DOAJ |
| description | Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results. |
| format | Article |
| id | doaj-art-d0e62eea73c74069864a4be865de6a20 |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-d0e62eea73c74069864a4be865de6a202025-08-20T02:42:48ZengMDPI AGEngineering Proceedings2673-45912023-12-0159110010.3390/engproc2023059100Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical ImagesChristodoss Prasanna Ranjith0Krishnamoorthy Natarajan1Sindhu Madhuri2Mahesh Thylore Ramakrishna3Chandrasekhar Rohith Bhat4Vinoth Kumar Venkatesan5Department of Information Technology, University of Technology and Applied Sciences -Shinas, Shinas 324, OmanSchool of Computer Science Engineering and Information Systems(SCORE), Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Information Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Bengaluru 560065, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru 562112, IndiaSaveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, IndiaSchool of Computer Science Engineering and Information Systems(SCORE), Vellore Institute of Technology, Vellore 632014, IndiaImage segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results.https://www.mdpi.com/2673-4591/59/1/100IoT (Internet of Things)medical image processingimage segmentationimage registrationadaptive k-means clustering method |
| spellingShingle | Christodoss Prasanna Ranjith Krishnamoorthy Natarajan Sindhu Madhuri Mahesh Thylore Ramakrishna Chandrasekhar Rohith Bhat Vinoth Kumar Venkatesan Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images Engineering Proceedings IoT (Internet of Things) medical image processing image segmentation image registration adaptive k-means clustering method |
| title | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
| title_full | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
| title_fullStr | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
| title_full_unstemmed | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
| title_short | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
| title_sort | image processing using feature based segmentation techniques for the analysis of medical images |
| topic | IoT (Internet of Things) medical image processing image segmentation image registration adaptive k-means clustering method |
| url | https://www.mdpi.com/2673-4591/59/1/100 |
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