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Localized Patch-Based Fuzzy Active Contours for Image Segmentation

This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose i...

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Detalles Bibliográficos
Autores principales: Fang, Jiangxiong, Liu, Hesheng, Liu, Huaxiang, Zhang, Liting, Liu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5187600/
https://www.ncbi.nlm.nih.gov/pubmed/28070210
http://dx.doi.org/10.1155/2016/1064692
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author Fang, Jiangxiong
Liu, Hesheng
Liu, Huaxiang
Zhang, Liting
Liu, Jun
author_facet Fang, Jiangxiong
Liu, Hesheng
Liu, Huaxiang
Zhang, Liting
Liu, Jun
author_sort Fang, Jiangxiong
collection PubMed
description This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy.
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spelling pubmed-51876002017-01-09 Localized Patch-Based Fuzzy Active Contours for Image Segmentation Fang, Jiangxiong Liu, Hesheng Liu, Huaxiang Zhang, Liting Liu, Jun Comput Math Methods Med Research Article This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy. Hindawi Publishing Corporation 2016 2016-12-13 /pmc/articles/PMC5187600/ /pubmed/28070210 http://dx.doi.org/10.1155/2016/1064692 Text en Copyright © 2016 Jiangxiong Fang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Fang, Jiangxiong
Liu, Hesheng
Liu, Huaxiang
Zhang, Liting
Liu, Jun
Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title_full Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title_fullStr Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title_full_unstemmed Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title_short Localized Patch-Based Fuzzy Active Contours for Image Segmentation
title_sort localized patch-based fuzzy active contours for image segmentation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5187600/
https://www.ncbi.nlm.nih.gov/pubmed/28070210
http://dx.doi.org/10.1155/2016/1064692
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