title: Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution.
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 α-stable distributions. A Bayesian α-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 α-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 α-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.
authors.affiliation: Department of Signal Theory, Networking and Communications, University of Granada, Spain. dsalas@ugr.es
authors.forename: D
authors.initials: D
authors.lastname: Salas-Gonzalez
authors.forename: J M
authors.initials: JM
authors.lastname: Górriz
authors.forename: J
authors.initials: J
authors.lastname: Ramírez
authors.forename: M
authors.initials: M
authors.lastname: Schloegl
authors.forename: E W
authors.initials: EW
authors.lastname: Lang
authors.forename: A
authors.initials: A
authors.lastname: Ortiz
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012815:Signal Processing, Computer-Assisted