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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (7): 520-533    DOI: 10.1631/jzus.C1100288
    
Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami
Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran; School of Computing, University of Kent, Canterbury CT2 7PD, UK
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Abstract  As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements. The results of the proposed method are evaluated using Dice similarity and Accuracy indices. Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.

Key words： Magnetic resonance imaging (MRI)      Segmentation      Fuzzy c-mean (FCM)      Dempster-Shafer theory (DST)     
Received: 07 October 2011      Published: 06 July 2012
CLC:  TP391  
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Jamal Ghasemi
Mohammad Reza Karami Mollaei
Reza Ghaderi
Ali Hojjatoleslami
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Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory. Front. Inform. Technol. Electron. Eng., 2012, 13(7): 520-533.

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http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C1100288     OR     http://www.zjujournals.com/xueshu/fitee/Y2012/V13/I7/520


Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements. The results of the proposed method are evaluated using Dice similarity and Accuracy indices. Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.

关键词： Magnetic resonance imaging (MRI),  Segmentation,  Fuzzy c-mean (FCM),  Dempster-Shafer theory (DST) 
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