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Studying Cerebral Vasculature Using Structure Proximity and Graph Kernels

  • Roland Kwitt
  • Danielle Pace
  • Marc Niethammer
  • Stephen Aylward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

An approach to study population differences in cerebral vasculature is proposed. This is done by 1) extending the concept of encoding cerebral blood vessel networks as spatial graphs and 2) quantifying graph similarity in a kernel-based discriminant classifier setup. We argue that augmenting graph vertices with information about their proximity to selected brain structures adds discriminative information and consequently leads to a more expressive encoding. Using graph-kernels then allows us to quantify graph similarity in a principled way. To demonstrate our approach, we assess the hypothesis that gender differences manifest as variations in the architecture of cerebral blood vessels, an observation that previously had only been tested and confirmed for the Circle of Willis. Our results strongly support this hypothesis, i.e, we can demonstrate non-trivial, statistically significant deviations from random gender classification in a cross-validation setup on 40 healthy patients.

Keywords

Cerebral Vasculature Cerebral Blood Vessel Vertex Label Functional Graph Graph Kernel 
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.

References

  1. 1.
    Aylward, S.R., Jomier, J., Vivert, C., LeDigarcher, V., Bullitt, E.: Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 59–66. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bullit, E., Zeng, D., Mortamet, B., Gosh, A., Aylward, S., Lin, W., Marks, B., Smith, K.: The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography. Neurobiol. Aging 31(2), 290–300 (2010)CrossRefGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM TIST 2(3), 1–27 (2011)CrossRefGoogle Scholar
  4. 4.
    Gerig, G., Koller, T., Szekely, G., Brechbühler, C., Käbler, O.: Symbolic description of 3D structures applied to cerebral vessel tree obtained from MR angiography volume data. In: Barrett, H.H., Gmitro, A.F. (eds.) IPMI 1993. LNCS, vol. 687, Springer, Heidelberg (1993)CrossRefGoogle Scholar
  5. 5.
    Golland, P., Fischl, B.: Permutation tests for classification: Towards statistical significance in image-based studies. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 330–341. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Halle, M., Talos, I.F., Jakab, M., Makris, N., Meier, D., Wald, L., Fischl, B., Kikinis, R.: Multi-modality MRI-based atlas of the brain (2012)Google Scholar
  7. 7.
    Horikoshi, T., Akiyama, I., Yamagata, Z., Sugita, M., Nukuki, H.: Magnetic resonsance angiographic evidence of sex-linked variations in the Circle of Willis and the occurrence of cerebral aneurysms. J. Neurosurg. 96, 697–703 (2002)CrossRefGoogle Scholar
  8. 8.
    Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., Borgwardt, K.: Weisfeiler-Lehman graph kernels. JMLR 12, 2539–2561 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roland Kwitt
    • 1
  • Danielle Pace
    • 1
  • Marc Niethammer
    • 2
  • Stephen Aylward
    • 1
  1. 1.Kitware Inc.CarrboroUSA
  2. 2.University of North Carolina (UNC)Chapel HillUSA

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