Classification of MRI brain images using k-nearest neighbor and artificial neural network

Magnetic resonance imaging (MRI) is often the medical imaging method of choice when soft tissue delineation is necessary. This paper presents a new approach for automated diagnosis based on classification of the magnetic resonance images (MRI). The proposed method consists of two stages namely feature extraction and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). Wavelet transform based methods are a well known tool for extracting frequency space information from non-stationary signals. The features extracted using DWT of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier is based on feed forward back propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. A classification with a success of 90% and 99% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.