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A multi-scale geometric flow for segmenting vasculature in MRI : theory and validation

McGill University
Publication Date
  • Health Sciences
  • Radiology.
  • Computer Science.
  • Computer Science
  • Mathematics


In this thesis we present a novel multi-scale geometric flow for segmenting vasculature from PD images which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. The key idea is to first apply Frangi's vesselness measure [Frangi et al. (1998)] to find putative centerlines of tubular structures along with their estimated radii. This multi-scale measure is then distributed to create a vector field which is orthogonal to vessel boundaries so that the flux maximizing flow algorithm of Vasilevskiy and Siddiqi (2002) can be applied to recover them. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We also validate the approach quantitatively on a data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, all obtained for the same subject. A significant finding is that over 80% of the vasculature recovered in the angiographic data sets is also recovered from the PD volume. Furthermore, over 25% of the vasculature recovered from the PD volume is not detectable in the TOF angiographic data.

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