Variational level set image segmentation model coupled with kernel distance function

One of the crucial challenges in the area of image segmentation is intensity inhomogeneity. For most of the region-based models, it is not easy to completely segment images having severe intensity inhomogeneity and complex structure, as they rely on intensity distributions. In this work, we proposed...

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Main Authors:	Noor Badshah, Ali Ahmad, Fazli Rehman
Format:	Article
Language:	English
Published:	SAGE Publishing 2020-06-01
Series:	Journal of Algorithms & Computational Technology
Online Access:	https://doi.org/10.1177/1748302620931421

id	doaj-093211e0d4c74c268d8dd183ef7e45b7
record_format	Article
spelling	doaj-093211e0d4c74c268d8dd183ef7e45b72020-11-25T03:23:36ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262020-06-011410.1177/1748302620931421Variational level set image segmentation model coupled with kernel distance functionNoor BadshahAli AhmadFazli RehmanOne of the crucial challenges in the area of image segmentation is intensity inhomogeneity. For most of the region-based models, it is not easy to completely segment images having severe intensity inhomogeneity and complex structure, as they rely on intensity distributions. In this work, we proposed a firsthand hybrid model by blending kernel and Euclidean distance metrics. Experimental results on some real and synthetic images suggest that our proposed model is better than models of Chan and Vese, Wu and He, and Salah et al.https://doi.org/10.1177/1748302620931421
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Noor Badshah Ali Ahmad Fazli Rehman
spellingShingle	Noor Badshah Ali Ahmad Fazli Rehman Variational level set image segmentation model coupled with kernel distance function Journal of Algorithms & Computational Technology
author_facet	Noor Badshah Ali Ahmad Fazli Rehman
author_sort	Noor Badshah
title	Variational level set image segmentation model coupled with kernel distance function
title_short	Variational level set image segmentation model coupled with kernel distance function
title_full	Variational level set image segmentation model coupled with kernel distance function
title_fullStr	Variational level set image segmentation model coupled with kernel distance function
title_full_unstemmed	Variational level set image segmentation model coupled with kernel distance function
title_sort	variational level set image segmentation model coupled with kernel distance function
publisher	SAGE Publishing
series	Journal of Algorithms & Computational Technology
issn	1748-3026
publishDate	2020-06-01
description	One of the crucial challenges in the area of image segmentation is intensity inhomogeneity. For most of the region-based models, it is not easy to completely segment images having severe intensity inhomogeneity and complex structure, as they rely on intensity distributions. In this work, we proposed a firsthand hybrid model by blending kernel and Euclidean distance metrics. Experimental results on some real and synthetic images suggest that our proposed model is better than models of Chan and Vese, Wu and He, and Salah et al.
url	https://doi.org/10.1177/1748302620931421
work_keys_str_mv	AT noorbadshah variationallevelsetimagesegmentationmodelcoupledwithkerneldistancefunction AT aliahmad variationallevelsetimagesegmentationmodelcoupledwithkerneldistancefunction AT fazlirehman variationallevelsetimagesegmentationmodelcoupledwithkerneldistancefunction
_version_	1724605559719591936

Variational level set image segmentation model coupled with kernel distance function

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