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Exploring Cortical Folding Pattern Variability Using Local Image Features

  • Rishi Rajalingham
  • Matthew Toews
  • D. Louis Collins
  • Tal Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)

Abstract

The variability in cortical morphology across subjects makes it difficult to develop a general atlas of cortical sulci. In this paper, we present a data-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be present in all subjects. The choice of feature type (SIFT, KLT, Harris-affine) is explored with regards to identifying cortical folding patterns while also uncovering their group-related variability across subjects. The model is built on lateral volume renderings from the ICBM dataset, and applied to hemisphere classification in order to identify patterns of lateralization based on each feature type.

Keywords

Model Feature Local Image Scale Invariant Feature Transform Folding Pattern Anatomical Variability 
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 2011

Authors and Affiliations

  • Rishi Rajalingham
    • 1
  • Matthew Toews
    • 2
  • D. Louis Collins
    • 3
  • Tal Arbel
    • 1
  1. 1.Center for Intelligent MachinesMcGill UniversityCanada
  2. 2.Brigham and Women’s HospitalHarvard Medical SchoolUSA
  3. 3.Montreal Neurological InstituteMcGill UniversityCanada

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