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Unsupervised Brain Tissue Segmentation by Using Bias Correction Fuzzy C-Means and Class-Adaptive Hidden Markov Random Field Modelling

  • Ziming Zeng
  • Chunlei Han
  • Liping Wang
  • Reyer Zwiggelaar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

Unsupervised brain tissue segmentation in magnetic resonance imaging (MRI) is a key step in brain analysis, such as computer-aided surgery, clinical diagnosis, pathological analysis, surgical planning. Due to the noise and bias field in MRI, it is difficult to automatically segment brain tissue. In order to improve the segmentation accuracy, we propose an unsupervised method which combines an improved bias correction Fuzzy C-means (BCFCM) and class-adaptive hidden markov random field Modelling (HMRF). The BCFCM segmentation result is used as the initial labelling for class-adaptive HMRF, which is utilized to refine the segmentation results. Experiments are evaluated on simulated MR images. Comparing with the ground truth, the results show that the proposed method can perform well on MR brain images with noisy MRI and bias field.

Keywords

MRI Brain Segmentation Noise Bias field 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ziming Zeng
    • 1
    • 2
  • Chunlei Han
    • 3
  • Liping Wang
    • 2
  • Reyer Zwiggelaar
    • 2
  1. 1.Information and Control Engineering FacultyShenyang Jianzhu UniversityShenyangChina
  2. 2.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  3. 3.Turku PET CenterTurku University HospitalTurkuFinland

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