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

Jamal GHASEMI; Mohammad Reza KARAMI MOLLAEI; Reza GHADERI; Ali HOJJATOLESLAMI

doi:10.1631/jzus.C1100288

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

Computer & Automation | Updated：2022-12-09

- Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
- Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 7, Pages: 520-533(2012)
- Affiliations：
  
  1.Signal Processing Laboratory, Faculty of Electrical and Computer Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran
  2.School of Computing, University of Kent, Canterbury CT2 7PD, UK
- Author bio：
  
  [ "Jamal GHASEMI, E-mail: jghasemi@stu.nit.ac.ir" ]
- Funds：
- DOI：10.1631/jzus.C1100288
  CLC： TP391
- Received：07 October 2011，
  
  Revised：2012-;5-;4，
  
  Published：2012-07
- Accepted：
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Jamal GHASEMI, Mohammad Reza KARAMI MOLLAEI, Reza GHADERI, et al. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(7): 520-533.
DOI：

Jamal GHASEMI, Mohammad Reza KARAMI MOLLAEI, Reza GHADERI, et al. Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(7): 520-533. DOI： 10.1631/jzus.C1100288.

摘要

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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references

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