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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
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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摘要： 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)    
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)
收稿日期: 2011-10-07 出版日期: 2012-07-06
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/CN/10.1631/jzus.C1100288    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I7/520

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