Neural Network based Segmentation of Magnetic ResonanceImages of the

This paper presents a study investigating the potential of artiicial neural networks (ANN's) for the classiication and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artiicial Neural Network (ANN) for the multispectral supervised classiication of MR images. We have modiied the LVQ for better and more accurate classiication. 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 diierent slices. It shows that tissue segmen-tation using LVQ ANN also performs better and faster than that using back-propagation ANN.