MRI Tissue Segmentation Using a Variational Multilayer Approach

  • Ginmo Chung
  • Ivo D. Dinov
  • Arthur W. Toga
  • Luminita A. Vese
Conference paper


We propose novel piecewise-constant minimization models for three-dimensional MRI brain data segmentation into white matter, gray matter, and cerebrospinal fluid. The proposed approaches are based on a multilayer or nested implicit surface evolution in variational form, well adapted to this problem. We propose two models, with and without using prior spatial information. The prior information is in the form of a probabilistic brain atlas, encoding spatial information of these three anatomical structures. Extensive experimental results and comparisons with manual segmentation and with automated segmentation are presented, together with quantitative assessment.


MRI image segmentation Variational level set methods Active surfaces Partial differential equations Multilayer approach 



This work was funded by the NIH through the NIH Roadmap for Medical Research, Grant U54 RR021813 entitled Center for Computational Biology (CCB).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ginmo Chung
    • 1
  • Ivo D. Dinov
    • 2
  • Arthur W. Toga
    • 2
  • Luminita A. Vese
    • 1
  1. 1.Department of MathematicsUniversity of CaliforniaLos AngelesUSA
  2. 2.Laboratory of Neuro ImagingUniversity of CaliforniaLos AngelesUSA

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