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Segmentation Of Brain Mr Images Using J-Divergence Based Active Contour Models

  • Wanlin Zhu
  • Tianzi Jiang
  • Xiaobo Li
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

In this chapter we propose a novel variational formulation for brain MRI segmentation. The originality of our approach is on the use of J-divergence (symmetrized Kullback-Leibler divergence) to measure the dissimilarity between local and global regions. In addition, a three-phase model is proposed to perform the segmentation task. The voxel intensity value of all regions is assumed to follow Gaussian distribution. It is introduced to ensure the robustness of the algorithm when an image is corrupted by noise. J-divergence is then used to measure the “distance” between the local and global region probability density functions. The proposed method yields promising results on synthetic and real brain MR images.

Keywords

Active Contour Active Contour Model Signed Distance Function Geometric Active Contour Model Geodesic Active Region 
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 Science+Business Media, LLC 2007

Authors and Affiliations

  • Wanlin Zhu
    • 1
  • Tianzi Jiang
    • 1
  • Xiaobo Li
    • 1
  1. 1.National Laboratory of Pattern RecognitionInstitute of AutomationChina

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