Bayes clustering and structural support vector machines for segmentation of carotid artery plaques in multicontrast MRI.
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Authors
Guan, Qiu
Du, Bin
Teng, Zhongzhao
Gillard, Jonathan https://orcid.org/0000-0003-4787-8091
Chen, Shengyong
Abstract
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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Keywords
Bayes Theorem, Carotid Arteries, Carotid Stenosis, Cluster Analysis, Contrast Media, Humans, Image Enhancement, Magnetic Resonance Imaging, Models, Statistical, Software, Support Vector Machine
Journal Title
Comput Math Methods Med
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Journal ISSN
1748-670X
1748-6718
1748-6718
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Publisher
Hindawi Limited