A Texture Manifold for Curve-Based Morphometry of the Cerebral Cortex

  • Maxime Boucher
  • Alan Evans
  • Kaleem Siddiqi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)


The cortical surface of the human brain is composed of folds that are juxtaposed alongside one another. Several methods have been proposed to study the shape of these folds, e.g., by first segmenting them on the cortical surface or by analysis via a continuous deformation of a common template. A major disadvantage of these methods is that, while they can localize shape differences, they cannot easily identify the directions in which they occur. The type of deformation that causes a fold to change in length is quite different from that which causes it to change in width. Furthermore, these two deformations may have a completely different biological interpretation. In this article we propose a method to analyze such deformations using directional filters locally adapted to the geometry of the folding pattern. Motivated by the texture flow literature in computer vision we recover flow fields that maintain a fixed angle with the orientation of folds, over a significant spatial extent. We then trace the flow fields to determine which correspond to the shape changes that are the most salient. Using the OASIS database, we demonstrate that in addition to known regions of atrophy, our method can find subtle but statistically significant shape deformations.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fischl, B., Sereno, M.I., Dale, A.M.: Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2), 195–207 (1999)CrossRefGoogle Scholar
  2. 2.
    Leporé, N., Brun, C., Pennec, X., Chou, Y., Lopez, O., Aizenstein, H., Becker, J., Toga, A., Thompson, P.: Mean template for tensor-based morphometry using deformation tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 826–833. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Lyttelton, O., Boucher, M., Robbins, S., Evans, A.: An unbiased iterative group registration template for cortical surface analysis. Neuroimage 34(4), 1535–1544 (2007)CrossRefGoogle Scholar
  4. 4.
    Worsley, K.: Testing for signals with unknown location and scale in a chi^ 2 random field, with an application to fMRI. Advances in Applied Probability 33(4), 773–793 (2001)MathSciNetMATHGoogle Scholar
  5. 5.
    Chung, M., Worsley, K., Evans, A.: Tensor-based brain surface modeling and analysis. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 467–473 (2003)Google Scholar
  6. 6.
    Ben-Shahar, O., Zucker, S.: The perceptual organization of texture flow: A contextual inference approach. PAMI 25(4), 401–417 (2003)CrossRefGoogle Scholar
  7. 7.
    Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., Buckner, R.: Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  8. 8.
    Hummel, R., Zucker, S.: On the foundations of relaxation labeling processes. In: Readings in computer vision: issues, problems, principles, and paradigms, p. 605. Morgan Kaufmann Publishers Inc., San Fransico (1987)Google Scholar
  9. 9.
    Adler, R., Taylor, J.: Random fields and geometry. Springer, New York (2007)MATHGoogle Scholar
  10. 10.
    Chung, M., Worsley, K., Robbins, S., Paus, T., Taylor, J., Giedd, J., Rapoport, J., Evans, A.: Deformation-based surface morphometry applied to gray matter deformation. NeuroImage 18(2), 198–213 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maxime Boucher
    • 1
  • Alan Evans
    • 2
  • Kaleem Siddiqi
    • 1
  1. 1.Center for Intelligent MachinesMcGill UniversityCanada
  2. 2.McConnell Brain Imaging CenterMcGill UniversityCanada

Personalised recommendations