Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis

  • Laurent Chauvin
  • Kuldeep Kumar
  • Christian Desrosiers
  • Jacques De Guise
  • Matthew Toews
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


This paper proposes a novel keypoint descriptor for Diffusion Weighted Image(DWI) analysis, the Diffusion Orientation Histogram (DOH). The DOH descriptor quantizes local diffusion gradients into histograms over spatial location and orientation, in a manner analogous to the quantization of image gradients in the widely used Histogram of Oriented Gradients (HOG) technique. Diffusion gradient symmetry allows representing half of the orientation space at double the angular resolution, leading to a compact but highly informative descriptor. Quantitative preliminary experiments evaluate descriptors for the task of automatically identifying familial links (twins, non-twin siblings) from DWI keypoint correspondences. The DOH descriptor is found to be complementary to traditional HOG descriptors computed from scalar fractional anisotropy (FA) images, where concatenated DOH and HOG descriptors result in the highest rates of correct family member identification. Twin-twin descriptor correspondences are generally more concentrated about major white matter tracts, e.g. the internal capsule, in comparison to twin/non-twin sibling correspondences.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Laurent Chauvin
    • 1
    • 2
  • Kuldeep Kumar
    • 1
  • Christian Desrosiers
    • 1
  • Jacques De Guise
    • 2
  • Matthew Toews
    • 1
  1. 1.Laboratory for Imagery, Vision and Artificial IntelligenceÉcole de Technologie SupérieureMontrealCanada
  2. 2.Laboratoire de recherche en imagerie et orthopédie (LIO)École de Technologie SupérieureMontrealCanada

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