Abstract
Automated and accurate classification of MR brain images is of importance for the analysis and interpretation of these images and many methods have been proposed. In this paper, we present a neural network (NN) based method to classify a given MR brain image as normal or abnormal. This method first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to a back propagation (BP) NN, with which scaled conjugate gradient (SCG) is adopted to find the optimal weights of the NN. We applied this method on 66 images (18 normal, 48 abnormal). The classification accuracies on both training and test images are 100%, and the computation time per image is only 0.0451 s.
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