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

Abstract

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, the authors present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. The authors have modified the LVQ for better and more accurate classification. They have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, the authors' method is insensitive to the gray-level variation of MR images between different slices. It shows that tissue segmentation using LVQ ANN also performs better and faster than that using back-propagation ANN.