title: Automatic selection of ROIs in functional imaging using Gaussian mixture models.
abstract: We present an automatic method for selecting regions of interest (ROIs) of the information contained in three-dimensional functional brain images using Gaussian mixture models (GMMs), where each Gaussian incorporates a contiguous brain region with similar activation. The novelty of the approach is based on approximating the grey-level distribution of a brain image by a sum of Gaussian functions, whose parameters are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each Gaussian or cluster is represented by a multivariate Gaussian function with a center coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases.
authors.affiliation: Departamento Teoría de la Se nal, Telemática y Comunicaciones, Universidad Granada, Spain. gorriz@ugr.es
authors.forename: J M
authors.initials: JM
authors.lastname: Górriz
authors.forename: A
authors.initials: A
authors.lastname: Lassl
authors.forename: J
authors.initials: J
authors.lastname: Ramírez
authors.forename: D
authors.initials: D
authors.lastname: Salas-Gonzalez
authors.forename: C G
authors.initials: CG
authors.lastname: Puntonet
authors.forename: E W
authors.initials: EW
authors.lastname: Lang
mesh_terms: D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*