Automatic segmentation of MR images using self-organizing feature mapping and neural networks

In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the self-organizing feature map (SOFM) artificial neural network (ANN) for feature mapping and generates a set of codebook vectors for each tissue class. Features are selected from three image spectra: T1, T2 and proton density (PD) weighted images. An algorithm has been developed for isolating the cerebrum from the head scan prior to the segmentation. To classify the map, we extend the network by adding an associative layer. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. Any unclassified tissues were remained as unknown tissue class.