Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation

  • Jason J. Corso
  • Eitan Sharon
  • Alan Yuille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.


Brain Tumor Class Label Saliency Function Class Membership Coarse Level 
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 2006

Authors and Affiliations

  • Jason J. Corso
    • 1
  • Eitan Sharon
    • 2
  • Alan Yuille
    • 2
  1. 1.Medical Imaging InformaticsUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of StatisticsUniversity of CaliforniaLos AngelesUSA

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