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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Main Authors: Zahurul S., Zahidul S., Jidin R.
Other Authors: 35746021600
Format: Conference Paper
Published: 2023
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Institution: Universiti Tenaga Nasional
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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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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
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
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.
author2 35746021600
author_facet 35746021600
Zahurul S.
Zahidul S.
Jidin R.
format Conference Paper
author Zahurul S.
Zahidul S.
Jidin R.
author_sort Zahurul S.
title An adept edge detection algorithm for human knee osteoarthritis images
title_short 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_sort adept edge detection algorithm for human knee osteoarthritis images
publishDate 2023
_version_ 1806425916675457024