A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Bayes Clustering and Structural Support Vector Machines for Segmentation of Carotid Artery Plaques in Multicontrast MRI
2012
Computational and Mathematical Methods in Medicine
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.
doi:10.1155/2012/549102
pmid:23365619
pmcid:PMC3536030
fatcat:mlnao2bx55d6tky4g53bv5qs7q