Neural network-based segmentation of magnetic resonance images of the brain

This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. We have modified the LVQ for better and more accurate classification. We have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, our method is insensitive to the gray-level variation of MR images between different slices, It shows that tissue segmentation using LVQ ANN also performs be ner and faster than that using back-propagation ANN.