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, pre-processing of MR images, skull Striping, feature extraction, feature reduction and classification. After histogram equalization of image, the features are extracted based on Dual-Tree Complex wavelet transformation (DTCWT). Then the features are reduced using principal component analysis (PCA). In the last stage two classification methods, k-means clustering and Probabilistic neural network (PNN) 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.