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Communication Dans Un Congrès Année : 2008

Diffusion Tensor Imaging and Deconvolution on Spaces of Positive Definite Symmetric Matrices

Résumé

Diffusion tensor imaging can be studied as a deconvolution density estimation problem on the space of positive definite symmetric matrices. We develop a nonparametric estimator for the common density function of a random sample of positive definite matrices. Our estimator is based on the Helgason-Fourier transform and its inversion, the natural tools for analysis of compositions of random positive definite matrices. Under smoothness conditions on the density of the intrinsic error in the random sample, we derive bounds on the rates of convergence of our nonparametric estimator to the true density.
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Dates et versions

inria-00632882 , version 1 (16-10-2011)

Identifiants

  • HAL Id : inria-00632882 , version 1

Citer

Peter S. Kim, Donald Richards. Diffusion Tensor Imaging and Deconvolution on Spaces of Positive Definite Symmetric Matrices. 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy, Oct 2008, New-York, United States. pp.140-149. ⟨inria-00632882⟩

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