An adept edge detection algorithm for human knee osteoarthritis images
Digital image processing comprises varieties of applications, where some of these used in medical image processing include convolution, edge detection as well as contrast enhancement. Efficient edge detection depends on choosing the threshold; the choice of threshold directly determines the results...
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2023
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| _version_ | 1833352038919962624 |
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| author | Zahurul S. Zahidul S. Jidin R. |
| author2 | 35746021600 |
| author_facet | 35746021600 Zahurul S. Zahidul S. Jidin R. |
| author_sort | Zahurul S. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Digital image processing comprises varieties of applications, where some of these used in medical image processing include convolution, edge detection as well as contrast enhancement. Efficient edge detection depends on choosing the threshold; the choice of threshold directly determines the results of edge detection. In this paper, Sobel edge detection operator and its enhanced algorithm are first discussed in terms of optimal thresholding in C language under Linux platform. It is implemented a competent execution time for this new enhanced algorithm to detect edges for human knee osteoarthritis images in different critical situations. The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method. Also, it is shown that the algorithm is very effective in case of noisy and blurs images. � 2010 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-30650 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
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| spelling | my.uniten.dspace-306502024-04-17T09:32:39Z An adept edge detection algorithm for human knee osteoarthritis images Zahurul S. Zahidul S. Jidin R. 35746021600 36024580800 6508169028 Edge detection Knee osteoarthritis Sobel operator Algorithms Computer operating systems Digital image storage Image processing Imaging systems Mathematical operators Signal analysis Signal detection Signal processing C language Contrast Enhancement Digital image processing Edge detection algorithms Execution time Human knee Knee osteoarthritis Linux platform Medical Image Processing Optimal thresholding Salient features Sobel edge detection Sobel method Sobel operator Edge detection Digital image processing comprises varieties of applications, where some of these used in medical image processing include convolution, edge detection as well as contrast enhancement. Efficient edge detection depends on choosing the threshold; the choice of threshold directly determines the results of edge detection. In this paper, Sobel edge detection operator and its enhanced algorithm are first discussed in terms of optimal thresholding in C language under Linux platform. It is implemented a competent execution time for this new enhanced algorithm to detect edges for human knee osteoarthritis images in different critical situations. The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method. Also, it is shown that the algorithm is very effective in case of noisy and blurs images. � 2010 IEEE. Final 2023-12-29T07:50:49Z 2023-12-29T07:50:49Z 2010 Conference Paper 10.1109/ICSAP.2010.53 2-s2.0-77952162106 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952162106&doi=10.1109%2fICSAP.2010.53&partnerID=40&md5=3b2d569c2fc6865dd49b6923e358df48 https://irepository.uniten.edu.my/handle/123456789/30650 5432947 375 379 Scopus |
| spellingShingle | Edge detection Knee osteoarthritis Sobel operator Algorithms Computer operating systems Digital image storage Image processing Imaging systems Mathematical operators Signal analysis Signal detection Signal processing C language Contrast Enhancement Digital image processing Edge detection algorithms Execution time Human knee Knee osteoarthritis Linux platform Medical Image Processing Optimal thresholding Salient features Sobel edge detection Sobel method Sobel operator Edge detection Zahurul S. Zahidul S. Jidin R. An adept edge detection algorithm for human knee osteoarthritis images |
| title | An adept edge detection algorithm for human knee osteoarthritis images |
| title_full | An adept edge detection algorithm for human knee osteoarthritis images |
| title_fullStr | An adept edge detection algorithm for human knee osteoarthritis images |
| title_full_unstemmed | An adept edge detection algorithm for human knee osteoarthritis images |
| title_short | An adept edge detection algorithm for human knee osteoarthritis images |
| title_sort | adept edge detection algorithm for human knee osteoarthritis images |
| topic | Edge detection Knee osteoarthritis Sobel operator Algorithms Computer operating systems Digital image storage Image processing Imaging systems Mathematical operators Signal analysis Signal detection Signal processing C language Contrast Enhancement Digital image processing Edge detection algorithms Execution time Human knee Knee osteoarthritis Linux platform Medical Image Processing Optimal thresholding Salient features Sobel edge detection Sobel method Sobel operator Edge detection |
| url_provider | http://dspace.uniten.edu.my/ |