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Segmentation of Medical Images of Different Modalities Using Distance Weighted C-V Model

  • Xiaozheng Liu
  • Wei Liu
  • Yan Xu
  • Yongdi Zhou
  • Junming Zhu
  • Bradley S. Peterson
  • Dongrong Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

Abstract

Region-based active contour model (ACM) has been extensively used in medical image segmentation and Chan & Vese’s (C-V) model is one of the most popular ACM methods. We propose to incorporate into the C-V model a weighting function to take into consideration the fact that different locations in an image with differing distances from the active contour have differing importance in generating the segmentation result, thereby making it a weighted C-V (WC-V) model. The theoretical properties of the model and our experiments both demonstrate that the proposed WC-V model can significantly reduce the computational cost while improve the accuracy of segmentation over the results using the C-V model.

Keywords

Corpus Callosum Image Segmentation Segmentation Result Active Contour Active Contour Model 
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

  • Xiaozheng Liu
    • 1
    • 4
  • Wei Liu
    • 1
  • Yan Xu
    • 1
  • Yongdi Zhou
    • 2
  • Junming Zhu
    • 3
  • Bradley S. Peterson
    • 4
  • Dongrong Xu
    • 4
  1. 1.Key Laboratory of Brain Functional Genomics, Ministry of Education, China & Shanghai Key Laboratory of Brain Functional GenomicsEast China Normal University Shanghai Key Laboratory of Magnetic ResonanceShanghaiChina
  2. 2.Department of NeurosurgeryJohns Hopkins UniversityBaltimoreUSA
  3. 3.Department of NeurosurgeryThe Second Affiliated Hospital of Zhejiang UniversityHangzhouChina
  4. 4.NYSPI Unit 74MRI Unit, Columbia University Dept of Psychiatry, & New York State Psychiatric InstituteNew YorkUSA

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