Er. Kischell et al., CLASSIFICATION OF BRAIN COMPARTMENTS AND HEAD-INJURY LESIONS BY NEURAL NETWORKS APPLIED TO MRI, Neuroradiology, 37(7), 1995, pp. 535-541
Citations number
27
Categorie Soggetti
Radiology,Nuclear Medicine & Medical Imaging",Neurosciences
An automatic, neural network-based approach was applied to segment nor
mal brain compartments and lesions on MR images. Two supervised networ
ks, backpropagation (BPN) and counterpropagation, and two unsupervised
networks, Kohonen learning vector quantizer and analog adaptive reson
ance theory, were trained on registered T2-weighted and proton density
images. The classes of interest were background, gray matter, white m
atter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and
''unknown.'' A comprehensive feature vector was chosen to discriminat
e these classes. The BPN combined with feature conditioning, multiple
discriminant analysis followed by Hotelling transform, produced the mo
st accurate and consistent classification results. Classifications of
normal brain compartments were generally in agreement with expert inte
rpretation of the images. Macrocystic encephalomalacia and gliosis wer
e recognized and, except around the periphery, classified in agreement
with the clinician's report used to train the neural network.