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

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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Main Authors: Qiu Guan, Bin Du, Zhongzhao Teng, Jonathan Gillard, Shengyong Chen
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2012/549102
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spelling doaj-4f02aa88cdd947d687513d878e2185472020-11-25T01:08:16ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182012-01-01201210.1155/2012/549102549102Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRIQiu Guan0Bin Du1Zhongzhao Teng2Jonathan Gillard3Shengyong Chen4College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaDepartment of Radiology, University of Cambridge, Hills Road, Cambridge CB2 0SP, UKDepartment of Radiology, University of Cambridge, Hills Road, Cambridge CB2 0SP, UKCollege of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaAccurate 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.http://dx.doi.org/10.1155/2012/549102
collection DOAJ
language English
format Article
sources DOAJ
author Qiu Guan
Bin Du
Zhongzhao Teng
Jonathan Gillard
Shengyong Chen
spellingShingle Qiu Guan
Bin Du
Zhongzhao Teng
Jonathan Gillard
Shengyong Chen
Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
Computational and Mathematical Methods in Medicine
author_facet Qiu Guan
Bin Du
Zhongzhao Teng
Jonathan Gillard
Shengyong Chen
author_sort Qiu Guan
title 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_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_sort bayes clustering and structural support vector machines for segmentation of carotid artery plaques in multicontrast mri
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2012-01-01
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.
url http://dx.doi.org/10.1155/2012/549102
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