Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images
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| Titel: | Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images |
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| Autoren: | Christodoss Prasanna Ranjith, Krishnamoorthy Natarajan, Sindhu Madhuri, Mahesh Thylore Ramakrishna, Chandrasekhar Rohith Bhat, Vinoth Kumar Venkatesan |
| Quelle: | Engineering Proceedings, Vol 59, Iss 1, p 100 (2023) |
| Verlagsinformationen: | MDPI AG |
| Publikationsjahr: | 2023 |
| Bestand: | Directory of Open Access Journals: DOAJ Articles |
| Schlagwörter: | IoT (Internet of Things), medical image processing, image segmentation, image registration, adaptive k-means clustering method, Engineering machinery, tools, and implements, TA213-215 |
| Beschreibung: | 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. |
| Publikationsart: | article in journal/newspaper |
| Sprache: | English |
| Relation: | https://www.mdpi.com/2673-4591/59/1/100; https://doaj.org/toc/2673-4591; https://doaj.org/article/d0e62eea73c74069864a4be865de6a20 |
| DOI: | 10.3390/engproc2023059100 |
| Verfügbarkeit: | https://doi.org/10.3390/engproc2023059100 https://doaj.org/article/d0e62eea73c74069864a4be865de6a20 |
| Dokumentencode: | edsbas.99E5F2BC |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | 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. |
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| DOI: | 10.3390/engproc2023059100 |