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 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.
College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China.
Qiu
Q
Guan
Bin
B
Du
Zhongzhao
Z
Teng
Jonathan
J
Gillard
Shengyong
S
Chen
D001499:Bayes Theorem; D002339:Carotid Arteries / Q000473:pathology*; D016893:Carotid Stenosis / Q000175:diagnosis / Q000473:pathology*; D016000:Cluster Analysis; D003287:Contrast Media / Q000494:pharmacology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D012984:Software; D060388:Support Vector Machine
D003287:Contrast Media