title: Segmentation of magnetic resonance brain images through discriminant analysis.
abstract: Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and on eight real patient data sets are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multispectrality in classifying brain tissues is discussed.
authors.affiliation: Istituto per le Applicazioni del Calcolo Mauro Picone CNR-Sezione di Napoli, Consiglio Nazionale delle Ricerche, Via Pietro Castellino 111, Napoli 80131, Italy. u.amato@iac.cnr.it
authors.forename: Umberto
authors.initials: U
authors.lastname: Amato
authors.forename: Michele
authors.initials: M
authors.lastname: Larobina
authors.forename: Anestis
authors.initials: A
authors.lastname: Antoniadis
authors.forename: Bruno
authors.initials: B
authors.lastname: Alfano
mesh_terms: D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D016002:Discriminant Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D011336:Probability; D012680:Sensitivity and Specificity