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Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
Joint Authors
Guan, Qiu
Teng, Zhongzhao
Du, Bin
Gillard, Jonathan
Chen, Sheng-yong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-19
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Guan, Qiu& Du, Bin& Teng, Zhongzhao& Gillard, Jonathan& Chen, Sheng-yong. 2012. Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-6.
https://search.emarefa.net/detail/BIM-480627
Modern Language Association (MLA)
Guan, Qiu…[et al.]. Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-6.
https://search.emarefa.net/detail/BIM-480627
American Medical Association (AMA)
Guan, Qiu& Du, Bin& Teng, Zhongzhao& Gillard, Jonathan& Chen, Sheng-yong. Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-6.
https://search.emarefa.net/detail/BIM-480627
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-480627