Diffeomorphic Spectral Matching of Cortical Surfaces

  • Herve Lombaert
  • Jon Sporring
  • Kaleem Siddiqi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Accurate matching of cortical surfaces is necessary in many neuroscience applications. In this context diffeomorphisms are often sought, because they facilitate further statistical analysis and atlas building. Present methods for computing diffeomorphisms are based on optimizing flows or on inflating surfaces to a common template, but they are often computationally expensive. It typically takes several hours on a conventional desktop computer to match a single pair of cortical surfaces having a few hundred thousand vertices. We propose a very fast alternative based on an application of spectral graph theory on a novel association graph. Our symmetric approach can generate a diffeomorphic correspondence map within a few minutes on high-resolution meshes while avoiding the sign and multiplicity ambiguities of conventional spectral matching methods. The eigenfunctions are shared between surfaces and provide a smooth parameterization of surfaces. These properties are exploited to compute differentials on highly folded cortical surfaces. Diffeomorphisms can thus be verified and invalid surface folding detected. Our method is demonstrated to attain a vertex accuracy that is at least as good as that of FreeSurfer and Spherical Demons but in only a fraction of their processing time. As a practical experiment, we construct an unbiased atlas of cortical surfaces with a speed several orders of magnitude faster than current methods.


Spectral Decomposition Cortical Surface Matching Accuracy Initial Reference Spectral Match 
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 2013

Authors and Affiliations

  • Herve Lombaert
    • 1
  • Jon Sporring
    • 1
    • 2
  • Kaleem Siddiqi
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontrealCanada
  2. 2.eScience Center, Computer ScienceUniversity of CopenhagenDenmark

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