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

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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.

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Authors and Affiliations

  1. Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran

    Jamal Ghasemi, Mohammad Reza Karami Mollaei & Reza Ghaderi

  2. School of Computing, University of Kent, Canterbury, CT2 7PD, UK

    Ali Hojjatoleslami

Authors
  1. Jamal Ghasemi
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  2. Mohammad Reza Karami Mollaei
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  3. Reza Ghaderi
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  4. Ali Hojjatoleslami
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Correspondence to Jamal Ghasemi.

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Ghasemi, J., Karami Mollaei, M.R., Ghaderi, R. et al. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory. J. Zhejiang Univ. - Sci. C 13, 520–533 (2012). https://doi.org/10.1631/jzus.C1100288

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  • DOI: https://doi.org/10.1631/jzus.C1100288

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