Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution

Authors:
D. Salas-Gonzalez;J. M. GóRriz;J. RamíRez;M. Schloegl;E. W. Lang;A. Ortiz
Affiliations:
Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Computational Intelligence and Machine Learning Group, University of Regensburg, Germany;Computational Intelligence and Machine Learning Group, University of Regensburg, Germany;Department of Communication Engineering, University of Málaga, Spain
Venue:
Computers in Biology and Medicine
Year:
2013

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Abstract

This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of @a-stable distributions. A Bayesian @a-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The @a-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the @a-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.