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.
CITATION STYLE
Marím-Fernández, M., Alberola-López, C., Ruiz-Alzola, J., & Westin, C. F. (2003). Regularization of diffusion tensor maps using a non-Gaussian markov random field approach. In Lecture Notes in Computer Science (Vol. 2879, pp. 92–100). https://doi.org/10.1007/978-3-540-39903-2_12
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