Classification of Brain MR Images using Texture Feature Extraction

Alzheimer’s disease (AD), is a degenerative disease which leads to memory loss and problems with thinking and behaviour.AD is a type of dementia which accounts for an estimated 60% to 80% of cases. Accurate diagnosis depends on the identification of discriminative features of AD. Recently, different feature extraction methods are used for the classification of AD. In this paper, we proposed a classification framework to select features, which are extracted using Gray-Level Co-occurrence Matrix (GLCM) method to distinguish between the AD and the Normal Control (NC). In order to evaluate the proposed method, we have performed evaluations on the MRI acquiring from the OASIS database. The proposed method yields an average testing accuracy of 75.71% which indicates that the proposed method can differentiate AD and NC satisfactorily.