Mixture of Segmenters with Discriminative Spatial Regularization and Sparse Weight Selection

  • Ting Chen
  • Baba C. Vemuri
  • Anand Rangarajan
  • Stephan J. Eisenschenk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


This paper presents a novel segmentation algorithm which automatically learns the combination of weak segmenters and builds a strong one based on the assumption that the locally weighted combination varies w.r.t. both the weak segmenters and the training images. We learn the weighted combination during the training stage using a discriminative spatial regularization which depends on training set labels. A closed form solution to the cost function is derived for this approach. In the testing stage, a sparse regularization scheme is imposed to avoid overfitting. To the best of our knowledge, such a segmentation technique has never been reported in literature and we empirically show that it significantly improves on the performances of the weak segmenters. After showcasing the performance of the algorithm in the context of atlas-based segmentation, we present comparisons to the existing weak segmenter combination strategies on a hippocampal data set.


Training Image Weighted Combination Training Stage Neighborhood Graph Sign Distance Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ting Chen
    • 1
  • Baba C. Vemuri
    • 1
  • Anand Rangarajan
    • 1
  • Stephan J. Eisenschenk
    • 2
  1. 1.Department of CISEUniversity of FloridaGainesvilleUSA
  2. 2.Department of NeurologyUniversity of FloridaGainesvilleUSA

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