Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47455
Title: Regularization of diffusion tensor maps using a non-Gaussian markov random field approach
Authors: Marím-Fernández, Marcos
Alberola-López, Carlos
Ruiz-Alzola, Juan 
Westin, Carl Fredrik
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Tensor Magnetic Resonance Image
Gaussian Noise Model
Brain White Matter
Large Eigenvalue
Linear Component
Issue Date: 2003
Journal: Lecture Notes in Computer Science 
Conference: 6th International Conference on Medical Image Computing and Computer-Assisted Intervention 
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.
URI: http://hdl.handle.net/10553/47455
ISSN: 0302-9743
Source: Lecture Notes in Computer Science[ISSN 0302-9743],v. 2879, p. 92-100
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