Automatic segmentation of cerebral MR images using artificial neural networks
Javad Alirezaie, M. Edward Jernigan, Claude Nahmias · IEEE Transactions on Nuclear Science · 1998
The authors present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Their scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labeled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches the authors applied the c-means algorithm to the problem.