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Cortical Correspondence with Probabilistic Fiber Connectivity

  • Ipek Oguz
  • Marc Niethammer
  • Josh Cates
  • Ross Whitaker
  • Thomas Fletcher
  • Clement Vachet
  • Martin Styner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5636)

Abstract

This paper presents a novel method of optimizing point-based correspondence among populations of human cortical surfaces by combining structural cues with probabilistic connectivity maps. The proposed method establishes a tradeoff between an even sampling of the cortical surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn’t constrained to be just spatial proximity, but uses local sulcal depth measurements as well as probabilistic connectivity maps, computed from DWI scans via a stochastic tractography algorithm, to enhance the correspondence definition. We propose a novel method for projecting this fiber connectivity information on the cortical surface, using a surface evolution technique. Our cortical correspondence method does not require a spherical parameterization. Experimental results are presented, showing improved correspondence quality demonstrated by a cortical thickness analysis, as compared to correspondence methods using spatial metrics as the sole correspondence criterion.

Keywords

Cortical Thickness Cortical Surface Minimum Description Length Connectivity Probability Correspondence Feature 
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 2009

Authors and Affiliations

  • Ipek Oguz
    • 1
  • Marc Niethammer
    • 1
    • 3
  • Josh Cates
    • 4
  • Ross Whitaker
    • 4
  • Thomas Fletcher
    • 4
  • Clement Vachet
    • 2
  • Martin Styner
    • 1
    • 2
  1. 1.Departments of Computer ScienceUSA
  2. 2.PsychiatryUSA
  3. 3.Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillUSA
  4. 4.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

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