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On-line Access: 2012-07-06

Received: 2011-10-07

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.7 P.520-533

http://doi.org/10.1631/jzus.C1100288


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


Author(s):  Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami

Affiliation(s):  Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran; more

Corresponding email(s):   jghasemi@stu.nit.ac.ir

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


Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Journal of Zhejiang University Science C, 2012, 13(7): 520-533.

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author="Jamal Ghasemi, Mohammad Reza Karami Mollaei, Reza Ghaderi, Ali Hojjatoleslami",
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year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100288"
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%T Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
%A Jamal Ghasemi
%A Mohammad Reza Karami Mollaei
%A Reza Ghaderi
%A Ali Hojjatoleslami
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 7
%P 520-533
%@ 1869-1951
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100288

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T1 - Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
A1 - Jamal Ghasemi
A1 - Mohammad Reza Karami Mollaei
A1 - Reza Ghaderi
A1 - Ali Hojjatoleslami
J0 - Journal of Zhejiang University Science C
VL - 13
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SP - 520
EP - 533
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100288


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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