MRI based automated diagnosis of Alzheimer's: Fusing 3D wavelet-features with clinical data.

This paper presents a novel algorithm for classification of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from the healthy controls (HC) using structural MRI. Feature extraction is based on discrete 3D wavelet transform followed by PCA for transforming the feature space into linearly uncorrelated variables. Linear SVM is used for classification purposes with clinical dementia rating used as the target vector. Proposed methodology is fully automated and independent of the annotation of region of interest. The importance of MRI, demographical data, neuro-psychiatric test scores and statistics calculated over the wavelet coefficients for the classification is studied. Proposed methodology is applied on 197 subjects from a public database. A classification accuracy of 95% was achieved for the case of HC vs AD. For the case of HC vs MCI, and MCI vs AD the classification accuracy of 78% and 81% were achieved. The results are compared with an existing state of the art technique.