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Automatic Estimation of the Number of Segmentation Groups Based on MI

  • Ziming Zeng
  • Wenhui Wang
  • Longzhi Yang
  • Reyer Zwiggelaar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

Clustering is important in medical imaging segmentation. The number of segmentation groups is often needed as an initial condition, but is often unknown. We propose a method to estimate the number of segmentation groups based on mutual information, anisotropic diffusion model and class-adaptive Gauss-Markov random fields. Initially, anisotropic diffusion is used to decrease the image noise. Subsequently, the class-adaptive Gauss-Markov modeling and mutual information are used to determine the number of segmentation groups. This general formulation enables the method to easily adapt to various kinds of medical images and the associated acquisition artifacts. Experiments on simulated, and multi-model data demonstrate the advantages of the method over the current state-of-the-art approaches.

Keywords

Mutual Information Segmentation Result Magnetic Resonance Data Markov Random Field Model Medical Imaging Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ziming Zeng
    • 1
    • 2
  • Wenhui Wang
    • 3
    • 4
  • Longzhi Yang
    • 1
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityUK
  2. 2.Faculty of Information and Control EngineeringShenyang Jianzhu UniversityLiaoningChina
  3. 3.Network Information Center of the Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhouChina
  4. 4.Key Laboratory of Medical Image ProcessingSouthern Medical UniversityGuangzhouChina

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