文献类型：期刊文献

英文题名：Robust fuzzy clustering using nonsymmetric student's t finite mixture model for MR image segmentation

作者：Zhu, Hongqing[1];Pan, Xu[1]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：175

期号：PartA

起止页码：500

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20154901646613);WOS:【SCI-EXPANDED(收录号:WOS:000367756600049)】；

基金：The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Natural Science Foundation of China under Grant number 61371150.

语种：英文

外文关键词：Segmentation; Student's t mixture model; Nonsymmetric distribution; ML information; Mean template; Fuzzy c-means

摘要：Accurate tissue segmentation from magnetic resonance (MR) images is an essential step in clinical practice. In this paper, we introduce a robust fuzzy clustering scheme for finite mixture model fitting, which exploits the merits of the mixture of the nonsymmetric Student's t-distribution and mean template to reduce the sensitivity of the segmentation results with respect to noise. This approach utilizes a fuzzy objective function regularized by the Kullbacic-Leibler (n) divergence term and sets the dissimilarity function as the negative log-likelihood of the nonsymmetric Student's t-distribution and mean template. The advantage of this fuzzy clustering scheme is that the spatial relationships among neighbouring pixels are taken into account with the help of the mean template so that the proposed method is more robust to noise than several other existing fuzzy c-means (FCM)-based algorithms. Another advantage is that the application of the nonsymmetric Student's t-distribution mixture model allows the proposed model to fit different shapes of observed data. Experiments using synthetic and real MR images show that the proposed model has considerably better segmentation accuracy and robustness against noise compared with several well-known finite mixture models. (C) 2015 Elsevier B.V. All rights reserved.