Automatic selection of ROIs in functional imaging using Gaussian mixture models.
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.
Górriz
J M
JM
Departamento Teoría de la Se nal, Telemática y Comunicaciones, Universidad Granada, Spain. gorriz@ugr.es
Lassl
A
A
Ramírez
J
J
Salas-Gonzalez
D
D
Puntonet
C G
CG
Lang
E W
EW
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*