title: A system for computing neuromorphometry from magnetic resonance images.
abstract: A new technique for the automated measurement of a variety of gross neuroanatomical structures (such as cerebral ventricles) is described. The system exploits the local statistical properties of dual-echo magnetic resonance images of the brain to produce a fully unsupervised classification of these images into a variety of tissue types. Following this, a simple region agglomeration procedure aggregates the segmented regions by class, and submits these regions to a computational-geometric analysis from which a wide variety of measures are derived.
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, USA. ian@brc.uconn.edu
authors.forename: I R
authors.initials: IR
authors.lastname: Greenshields
authors.forename: F
authors.initials: F
authors.lastname: DiMario
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000295:instrumentation*; D002508:Cephalometry / Q000295:instrumentation; D002540:Cerebral Cortex / Q000033:anatomy & histology; D002552:Cerebral Ventricles / Q000033:anatomy & histology; D002555:Cerebrospinal Fluid / Q000502:physiology; D002648:Child; D004292:Dominance, Cerebral / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D008432:Mathematical Computing; D012016:Reference Values