Alzheimer's Disease Detection by Utilizing Key Slice Selection in 3D MRI Images

Abstract

This study proposes a new approach for improving the accuracy of high-dimensional pattern recognition problem of Alzheimer's Disease (AD). The proposed method uses information from three-dimensional Magnetic Resonance Imaging (MRI) brain data with 2D slices in three orthogonal directions. It includes the calculation of Fisher Criterion between the AD and Healthy Control (HC) groups in order to select key-slices in the coronal, sagittal and axial directions. The preprocessing phase involves 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.