ROI Analysis Using Harvard-Oxford Atlas in Alzheimer's Disease Diagnosis Based on PCA

Alzheimer's disease (AD) is characterized by impaired glucose metabolism and can be detected using Positron Emission Tomography (PET) neuroimaging. In this study, an automatic method for diagnosis of AD based on region of interest (ROI) is presented. First, subject’s PET neuroimage is automatically parcellated into 48 predefined ROIs using Harvard-Oxford structural Atlas. The most discriminative regions are discovered using principal component analysis (PCA). Based on features extracted using PCA, support vector machines are adapted to discriminate normal control (NC) from AD. For classification of AD from NC, the proposed method achieves 89.14% of classification accuracy; while the accuracy of Automated Anatomical Labeling (AAL)-based approach is only 80.68%.