A hybrid level set model for image segmentation.

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and l...

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Autores principales:	Weiqin Chen, Changjiang Liu, Anup Basu, Bin Pan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f324a1ea6d2e48f3be42ddae4032c3aa
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id	oai:doaj.org-article:f324a1ea6d2e48f3be42ddae4032c3aa
record_format	dspace
spelling	oai:doaj.org-article:f324a1ea6d2e48f3be42ddae4032c3aa2021-12-02T20:03:55ZA hybrid level set model for image segmentation.1932-620310.1371/journal.pone.0251914https://doaj.org/article/f324a1ea6d2e48f3be42ddae4032c3aa2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251914https://doaj.org/toc/1932-6203Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.Weiqin ChenChangjiang LiuAnup BasuBin PanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0251914 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weiqin Chen Changjiang Liu Anup Basu Bin Pan A hybrid level set model for image segmentation.
description	Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.
format	article
author	Weiqin Chen Changjiang Liu Anup Basu Bin Pan
author_facet	Weiqin Chen Changjiang Liu Anup Basu Bin Pan
author_sort	Weiqin Chen
title	A hybrid level set model for image segmentation.
title_short	A hybrid level set model for image segmentation.
title_full	A hybrid level set model for image segmentation.
title_fullStr	A hybrid level set model for image segmentation.
title_full_unstemmed	A hybrid level set model for image segmentation.
title_sort	hybrid level set model for image segmentation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f324a1ea6d2e48f3be42ddae4032c3aa
work_keys_str_mv	AT weiqinchen ahybridlevelsetmodelforimagesegmentation AT changjiangliu ahybridlevelsetmodelforimagesegmentation AT anupbasu ahybridlevelsetmodelforimagesegmentation AT binpan ahybridlevelsetmodelforimagesegmentation AT weiqinchen hybridlevelsetmodelforimagesegmentation AT changjiangliu hybridlevelsetmodelforimagesegmentation AT anupbasu hybridlevelsetmodelforimagesegmentation AT binpan hybridlevelsetmodelforimagesegmentation
_version_	1718375638382936064

A hybrid level set model for image segmentation.

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