Sulci Detection in Photos of the Human Cortex Based on Learned Discriminative Dictionaries

  • Benjamin Berkels
  • Marc Kotowski
  • Martin Rumpf
  • Carlo Schaller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)


The use of discriminative dictionaries is exploited for the segmentation of sulci in digital photos of the human cortex. Manual segmentation of the geometry of sulci by an experienced physician on training data is taken into account to build pairs of such dictionaries. It is demonstrated that this approach allows a robust segmentation of these brain structures on photos of the brain as long as the training data contains sufficiently similar images. Concerning the methodology an improved minimization algorithm for the underlying variational approach is presented taking into account recent advances in orthogonal matching pursuit. Furthermore, the method is stable since it ensures an energy decay in the dictionary update.


Patch Size Sparse Representation Manual Segmentation Energy Decay Dictionary Learning 
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 2012

Authors and Affiliations

  • Benjamin Berkels
    • 1
  • Marc Kotowski
    • 2
  • Martin Rumpf
    • 3
  • Carlo Schaller
    • 2
  1. 1.Interdisciplinary Mathematics InstituteUniversity of South CarolinaColumbiaUSA
  2. 2.Hôpitaux Universitaires de GenèveGenèveSwitzerland
  3. 3.Institut für Numerische SimulationRheinische Friedrich-Wilhelms-Universität BonnBonnGermany

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