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Brain MR Image Segmentation and Bias Field Correction through Class-K HMRF Model and EM Algorithm

  • Zexuan Ji
  • Quansen Sun
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

Accurate brain tissue segmentation from magnetic resonance (MR) images plays an important role in both clinical practice and neuroscience research. In this paper, we extend the hidden Markov random field (HMRF) model, and propose a novel model, called Class-K HMRF model, to further improve the segmentation accuracy by incorporating more contextual information during classification. This model simultaneously takes account of spatial dependencies between image pixels and bias field, and hence can overcome the difficulties caused by noise and intensity inhomogeneity. By comparing our algorithm with state-of-the-art approaches, the experimental results demonstrate that the proposed algorithm can produce more accurate and reliable segmentations.

Keywords

MRI segmentation bias field correction hidden Markov random field expectation-maximization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zexuan Ji
    • 1
  • Quansen Sun
    • 1
  1. 1.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingChina

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