An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering

This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. B...

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Autores principales:	Shafiullah Soomro, Asad Munir, Asif Aziz, Toufique Ahmed Soomro, Kwang Nam Choi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Active contours bias field level set clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/efd9c234b8a24372af4a33ad93723b2c
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spelling	oai:doaj.org-article:efd9c234b8a24372af4a33ad93723b2c2021-11-09T00:02:50ZAn Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering2169-353610.1109/ACCESS.2021.3122535https://doaj.org/article/efd9c234b8a24372af4a33ad93723b2c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585124/https://doaj.org/toc/2169-3536This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. Besides, to avoid local fitting terms being stuck into a local minimum, we use the swap function to interchange the fitting terms so that fitting values inside the object are always higher. Later for the elimination of the costly contour initialization that existed in previous level set based evolutions, we integrate kernel based fuzzy c-means clustering and intensity-based thresholding framework with the proposed framework to automate the proposed strategy. Finally, for the level set function regularization and the for the elimination of its re- initialization we have used the Gaussian function in the level set evolution. We demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of adaptive active contour model. We have additionally performed statistical analysis on real images and BRATS dataset using Dice index, accuracy, sensitivity, specificity and Jaccard index metrics. Results show that the proposed method gets high Dice index, accuracy, sensitivity, specificity and Jaccard index values compared to the previous state of art methods.Shafiullah SoomroAsad MunirAsif AzizToufique Ahmed SoomroKwang Nam ChoiIEEEarticleActive contoursbias fieldlevel setclusteringElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147016-147028 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Active contours bias field level set clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Active contours bias field level set clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shafiullah Soomro Asad Munir Asif Aziz Toufique Ahmed Soomro Kwang Nam Choi An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
description	This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. Besides, to avoid local fitting terms being stuck into a local minimum, we use the swap function to interchange the fitting terms so that fitting values inside the object are always higher. Later for the elimination of the costly contour initialization that existed in previous level set based evolutions, we integrate kernel based fuzzy c-means clustering and intensity-based thresholding framework with the proposed framework to automate the proposed strategy. Finally, for the level set function regularization and the for the elimination of its re- initialization we have used the Gaussian function in the level set evolution. We demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of adaptive active contour model. We have additionally performed statistical analysis on real images and BRATS dataset using Dice index, accuracy, sensitivity, specificity and Jaccard index metrics. Results show that the proposed method gets high Dice index, accuracy, sensitivity, specificity and Jaccard index values compared to the previous state of art methods.
format	article
author	Shafiullah Soomro Asad Munir Asif Aziz Toufique Ahmed Soomro Kwang Nam Choi
author_facet	Shafiullah Soomro Asad Munir Asif Aziz Toufique Ahmed Soomro Kwang Nam Choi
author_sort	Shafiullah Soomro
title	An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
title_short	An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
title_full	An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
title_fullStr	An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
title_full_unstemmed	An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering
title_sort	active contour method based on regularized kernel fuzzy c-means clustering
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/efd9c234b8a24372af4a33ad93723b2c
work_keys_str_mv	AT shafiullahsoomro anactivecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT asadmunir anactivecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT asifaziz anactivecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT toufiqueahmedsoomro anactivecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT kwangnamchoi anactivecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT shafiullahsoomro activecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT asadmunir activecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT asifaziz activecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT toufiqueahmedsoomro activecontourmethodbasedonregularizedkernelfuzzycmeansclustering AT kwangnamchoi activecontourmethodbasedonregularizedkernelfuzzycmeansclustering
_version_	1718441437702389760

An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering

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