Classification of brain compartments and head injury lesions by neural networks applied to MRI

E. R. Kischell, N. Kehtarnavaz, G. R. Hillman, H. Levin, M. Lilly, T. A. Kent

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and "unknown." A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.

Original languageEnglish (US)
Pages (from-to)535-541
Number of pages7
Issue number7
StatePublished - Oct 1995


  • Head injury
  • Magnetic resonance imaging
  • Neural networks

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine


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