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This article has been retracted upon the authors request as it was found to include unreliable interpretation due to insufficient provision of studying materials.

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References

  1. Q. Guan, B. Du, Z. Teng, J. Gillard, and S. Chen, “Bayes clustering and structural support vector machines for segmentation of carotid artery plaques in multicontrast MRI,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 549102, 6 pages, 2012.
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 549102, 6 pages
http://dx.doi.org/10.1155/2012/549102
Research Article

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

1College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China
2Department of Radiology, University of Cambridge, Hills Road, Cambridge CB2 0SP, UK

Received 6 October 2012; Accepted 19 November 2012

Academic Editor: Carlo Cattani

Copyright © 2012 Qiu Guan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.