Detection of Alzheimer’s Disease using 3D MRI Based on Key Slices Selected

Alzheimer’s disease (AD) is one of the most common irreversible dementia disease affecting mostly old people specially in older adulthood. This disease corresponds to a particular group of aging dementia which deteriorates long-term and short-term memory, behavior and thinking. Currently there is no treatment for AD, but early detection of AD can help finding out mechanisms of AD and make better life for patients who suffer from AD. In this study we detect Alzheimer’s disease (AD) subjects among elderly cohorts including health and AD subjects. One of the main issues of automatic AD classification is feature extraction in high-dimensional feature space. This thesis proposes new feature extraction methods for high-dimensional pattern recognition problem aimed at accurate detection of AD. The proposed methods use information from three dimensional magnetic resonance imaging (MRI) brain data with 2D slices in three orthogonal directions. The proposed method includes the calculation of Fisher Criterion between the AD and HC groups in order to select key-slices in the coronal, sagittal and axial directions. The preprocessing phase involve region detection to segment region of interest (ROI) based on displacement field (DF) method. Then we utilized energy, contrast and homogeneity metrics along with feature vectors generated by PCA and probability distribution function (PDF) methods in feature extraction phase for each key-slice selected in the earlier phase. Features coming from each key-slice are combined through feature fusion for improved accuracy. Experimental results show fusion method that used with brain mask give us the higher or comparable results compared with other feature extraction techniques in the literature. Keywords: Alzheimer’s disease, MRI, region of interest, Statistical feature extraction, data fusion, classification, support vector machine