A Robust Brain MRI Classification with GLCM Features

Automated and accurate classification of brain MRI is such important that leads us to present a new robust classification technique for analyzing magnetic response images. The proposed method consists of three stages, namely, feature extraction, dimensionality reduction, and classification. We use gray level co-occurrence matrix (GLCM) to extract features from brain MRI and for selecting the best features, PCA+LDA is implemented. The classifiers goal is to classify subjects as normal and abnormal brain MRI. A classification with a success of 100% for two normal and abnormal classes is obtained by the both classifiers based on artificial neural network (ANN) and k-nearest neighbor (k-NN). The proposed method leads to a robust and effective technique, which reduces the computational complexity, and the operational time compared with other recent works. General Terms Pattern Recognition, Classification.