Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI

Accurate segmentation of carotid artery plaque in MR images is not only a key part but also an essential step for in vivo plaque analysis. Due to the indistinct MR images, it is very difficult to implement the automatic segmentation. Two kinds of classification models, that is, Bayes clustering and...

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Detalles Bibliográficos
Autores principales: Guan, Qiu, Du, Bin, Teng, Zhongzhao, Gillard, Jonathan, Chen, Shengyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3536030/
https://www.ncbi.nlm.nih.gov/pubmed/23365619
http://dx.doi.org/10.1155/2012/549102
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author Guan, Qiu
Du, Bin
Teng, Zhongzhao
Gillard, Jonathan
Chen, Shengyong
author_facet Guan, Qiu
Du, Bin
Teng, Zhongzhao
Gillard, Jonathan
Chen, Shengyong
author_sort Guan, Qiu
collection PubMed
description Accurate segmentation of carotid artery plaque in MR images is not only a key part but also an essential step for in vivo plaque analysis. Due to the indistinct MR images, it is very difficult to implement the automatic segmentation. Two kinds of classification models, that is, Bayes clustering and SSVM, are introduced in this paper to segment the internal lumen wall of carotid artery. The comparative experimental results show the segmentation performance of SSVM is better than Bayes.
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spelling pubmed-35360302013-01-30 Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI Guan, Qiu Du, Bin Teng, Zhongzhao Gillard, Jonathan Chen, Shengyong Comput Math Methods Med Research Article Accurate segmentation of carotid artery plaque in MR images is not only a key part but also an essential step for in vivo plaque analysis. Due to the indistinct MR images, it is very difficult to implement the automatic segmentation. Two kinds of classification models, that is, Bayes clustering and SSVM, are introduced in this paper to segment the internal lumen wall of carotid artery. The comparative experimental results show the segmentation performance of SSVM is better than Bayes. Hindawi Publishing Corporation 2012 2012-12-19 /pmc/articles/PMC3536030/ /pubmed/23365619 http://dx.doi.org/10.1155/2012/549102 Text en Copyright © 2012 Qiu Guan 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
Guan, Qiu
Du, Bin
Teng, Zhongzhao
Gillard, Jonathan
Chen, Shengyong
Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title_full Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title_fullStr Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title_full_unstemmed Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title_short Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
title_sort bayes clustering and structural support vector machines for segmentation of carotid artery plaques in multicontrast mri
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3536030/
https://www.ncbi.nlm.nih.gov/pubmed/23365619
http://dx.doi.org/10.1155/2012/549102
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