MRI brain segmentation using hidden Markov random fields with alpha-stable distributions
Ignacio Peis, Ignacio A. Illán, Francisco J. Martínez-Murcia, F. Segovia, J. M. Górriz, Javier Ramı́rez, Elmar Wolfgang Lang, Diego Salas-Gonzalez · 2016
A MRI brain image segmentation method using a hidden Markov random fields with heavy-tailed alpha-stable distributions is presented. Each brain tissue is modelled using an alpha-stable distribution. Then, a HMRF is used to include spatial information in the classification model. The Gaussian distribution has been widely used for the modelization of the cerebrospinal fluid, white matter and gray matter. Nevertheless, the alpha-stable distribution has been recently proposed as a more accurate alternative for this task. The alpha-stable distribution is more impulsive and is also able to model the asymmetry and heavy-tails of the histogram of the brain tissues. We have tested the proposed methodology in 18 MR images from the Internet Brain Segmentation Repository. The proposed methodology outperforms the segmentation results obtained when a Gaussian model for the histogram of the brain tissues is considered. Furthermore, as the Normal distribution is a particulaar case of alpha-stable distribution. Therefore, the proposed approach is also a generalization of the hidden Markov random field segmentation method with Gaussian distributions.