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Tensor-based cortical surface morphometry via weighted spherical harmonic representation
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DOI：10.1109/TMI.2008.918338.png)
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Abstract
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We present a new tensor-based morphometric framework that quantities cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims or this paper are to present the weighted-SPHARNI and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.
Keywords:
cortical surface
spherical harmonic (SPHARM)
tensor-based morphometry
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