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- W2100177310 abstract "We present a new tensor-based morphometric framework that quantifies 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 of this paper are to present the weighted-SPHARM 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." @default.
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- W2100177310 date "2008-08-01" @default.
- W2100177310 modified "2023-09-27" @default.
- W2100177310 title "Tensor-Based Cortical Surface Morphometry via Weighted Spherical Harmonic Representation" @default.
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- W2100177310 doi "https://doi.org/10.1109/tmi.2008.918338" @default.
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