Sulci detection in photos of the human cortex based on learned discriminative dictionaries

  • Authors:
  • Benjamin Berkels;Marc Kotowski;Martin Rumpf;Carlo Schaller

  • Affiliations:
  • Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC;Hôpitaux Universitaires de Genève, Genève, Switzerland;Institut für Numerische Simulation, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany;Hôpitaux Universitaires de Genève, Genève, Switzerland

  • Venue:
  • SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
  • Year:
  • 2011

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Abstract

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.