A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

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 a...

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Bibliographic Details
Main Authors: Diego Castillo-Barnes, Ignacio Peis, Francisco J. Martínez-Murcia, Fermín Segovia, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Diego Salas-Gonzalez
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-11-01
Series:Frontiers in Neuroinformatics
Subjects:
magnetic resonance image
brain tissue segmentation
gray matter
white matter
α-stable distribution
hidden Markov random fields
Online Access:http://journal.frontiersin.org/article/10.3389/fninf.2017.00066/full

Internet

http://journal.frontiersin.org/article/10.3389/fninf.2017.00066/full

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