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Approximation of optimal surface parameterizations and the application in cerebral cortex mapping

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Abstract

Optimal parameterizations of surface meshes are useful in the mapping and visualization of the cerebral cortex, the outer layer of the human brain. We propose two new methods to compute approximations of the optimal parameterizations, and apply these methods to human cortical surface meshes extracted from magnetic resonance images. Our methods approximate the parameterizations in a low-dimensional subspace spanned by the coordinate vectors of an initial parameterization and the low-frequency eigenvectors of a mesh Laplacian. This low-dimensional approximation reduces the computational complexity while minimizing the error.

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Authors and Affiliations

  1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA

    Fijoy Vadakkumpadan

  2. Department of Mathematics, Technical University Darmstadt, Schloßgartenstraße 7, 64289, Darmstadt, Germany

    Peter Spellucci

  3. Department of Computer Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Yinlong Sun

Authors
  1. Fijoy Vadakkumpadan
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  2. Peter Spellucci
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  3. Yinlong Sun
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Correspondence to Fijoy Vadakkumpadan.

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Vadakkumpadan, F., Spellucci, P. & Sun, Y. Approximation of optimal surface parameterizations and the application in cerebral cortex mapping. Brain Struct Funct 212, 497–511 (2008). https://doi.org/10.1007/s00429-008-0179-z

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  • DOI: https://doi.org/10.1007/s00429-008-0179-z

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