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MICCAI
2003
Springer

Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach

10 years 6 months ago
Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach
Abstract. In this paper we propose a novel non-Gaussian MRF for regularization of tensor fields for fiber tract enhancement. Two entities are considered in the model, namely, the linear component of the tensor, i.e., how much line-like the tensor is, and the angle of the eigenvector associated to the largest eigenvalue. A novel, to the best of the author's knowledge, angular density function has been proposed. Closed form expressions of the posterior densities are obtained. Some experiments are also presented for which color-coded images are visually meaningful. Finally, a quantitative measure of regularization is also calculated to validate the achieved results based on an averaged measure of entropy.
Marcos Martín-Fernández, Carlos Albe
Added 15 Nov 2009
Updated 15 Nov 2009
Type Conference
Year 2003
Where MICCAI
Authors Marcos Martín-Fernández, Carlos Alberola-López, Juan Ruiz-Alzola, Carl-Fredrik Westin
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