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Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set for...

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Detalles Bibliográficos
Autores principales: Liu, Tingting, Xu, Haiyong, Jin, Wei, Liu, Zhen, Zhao, Yiming, Tian, Wenzhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4083809/
https://www.ncbi.nlm.nih.gov/pubmed/25028593
http://dx.doi.org/10.1155/2014/890725
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author Liu, Tingting
Xu, Haiyong
Jin, Wei
Liu, Zhen
Zhao, Yiming
Tian, Wenzhe
author_facet Liu, Tingting
Xu, Haiyong
Jin, Wei
Liu, Zhen
Zhao, Yiming
Tian, Wenzhe
author_sort Liu, Tingting
collection PubMed
description A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.
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spelling pubmed-40838092014-07-15 Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model Liu, Tingting Xu, Haiyong Jin, Wei Liu, Zhen Zhao, Yiming Tian, Wenzhe Comput Math Methods Med Research Article A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model. Hindawi Publishing Corporation 2014 2014-06-16 /pmc/articles/PMC4083809/ /pubmed/25028593 http://dx.doi.org/10.1155/2014/890725 Text en Copyright © 2014 Tingting Liu et al. https://creativecommons.org/licenses/by/3.0/ 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
Liu, Tingting
Xu, Haiyong
Jin, Wei
Liu, Zhen
Zhao, Yiming
Tian, Wenzhe
Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title_full Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title_fullStr Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title_full_unstemmed Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title_short Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model
title_sort medical image segmentation based on a hybrid region-based active contour model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4083809/
https://www.ncbi.nlm.nih.gov/pubmed/25028593
http://dx.doi.org/10.1155/2014/890725
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