title: Segmentation of magnetic resonance images using an artificial neural network.
abstract: Signal intensities from intermediate and T2 weighted spin echo images of the brain were used as inputs into an artificial neural network (ANN). The signal intensities were used to train the network to recognize anatomically-important segments. The ANN was a self-organizing map (SOM) neural network which develops a continuous topographical map of the signal intensities within the two images. The neural network segmented images demonstrated good correlation with white matter, gray matter, and cerebral spinal fluid (CSF) spaces. This technique was rated better than manual thresholding of the intermediate images, but not as good as manual thresholding of the T2 weighted images.
authors.affiliation: Department of Radiology, Cleveland Clinic Foundation.
authors.forename: D W
authors.initials: DW
authors.lastname: Piraino
authors.forename: S C
authors.initials: SC
authors.lastname: Amartur
authors.forename: B J
authors.initials: BJ
authors.lastname: Richmond
authors.forename: J P
authors.initials: JP
authors.lastname: Schils
authors.forename: J M
authors.initials: JM
authors.lastname: Thome
authors.forename: P B
authors.initials: PB
authors.lastname: Weber
mesh_terms: D001921:Brain / Q000473:pathology*; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer