Possibilistic-clustering-based MR brain image segmentation with accurate initialization

Qingmin Liao; Yingying Deng; Weibei Dou; Su Ruan; Daniel Bloyet

doi:10.1117/12.526800

18 January 2004 Possibilistic-clustering-based MR brain image segmentation with accurate initialization

Qingmin Liao, Yingying Deng, Weibei Dou, Su Ruan, Daniel Bloyet

Proceedings Volume 5308, Visual Communications and Image Processing 2004; (2004) https://doi.org/10.1117/12.526800
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Magnetic resonance image analysis by computer is useful to aid diagnosis of malady. We present in this paper a automatic segmentation method for principal brain tissues. It is based on the possibilistic clustering approach, which is an improved fuzzy c-means clustering method. In order to improve the efficiency of clustering process, the initial value problem is discussed and solved by combining with a histogram analysis method. Our method can automatically determine number of classes to cluster and the initial values for each class. It has been tested on a set of forty MR brain images with or without the presence of tumor. The experimental results showed that it is simple, rapid and robust to segment the principal brain tissues.

Citation Download Citation

Qingmin Liao, Yingying Deng, Weibei Dou, Su Ruan, and Daniel Bloyet "Possibilistic-clustering-based MR brain image segmentation with accurate initialization", Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); https://doi.org/10.1117/12.526800

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