A new approach to MRI brain images classification

The aim of this work is to present an automated method that assists diagnosis of normal and abnormal MR images. The diagnosis method consists of four stages, preprocessing of MR images, feature extraction, dimensionality reduction and classification. After histogram equalization of image, the features are extracted based on discrete wavelet transformation (DWT). Then the features are reduced using principal component analysis (PCA). In the last stage three classification methods, k-nearest neighbour (k-NN), parzen window and artificial neural network (ANN) are employed. Our work is the modification and extension of the previous studies on the diagnosis of brain diseases, while we obtain better classification rate with the less number of features and we also use larger and rather different database.