TY - JOUR AU - Castillo-Barnes, Diego AU - Peis, Ignacio AU - Martínez-Murcia, Francisco J. AU - Segovia, Fermín AU - Illán, Ignacio A. AU - Górriz, Juan M. AU - Ramírez, Javier AU - Salas-Gonzalez, Diego PY - 2017 M3 - Original Research TI - A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI JO - Frontiers in Neuroinformatics UR - https://www.frontiersin.org/articles/10.3389/fninf.2017.00066 VL - 11 SN - 1662-5196 N2 - A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI. ER -