干旱区生态环境知识资源中心

The early identification of mild cognitive impairment (MCI) has the potential for timely therapeutic interventions that would limit the advancement of MCI to Alzheimer’s disease (AD). This paper presents an automated approach for early detection of MCI through pattern classification of magnetic resonance imaging (MRI) data. The approach is based on image feature selection and support vector machine (SVM) classification. Subjects were selected from the Open Access Series of Imaging Studies (OASIS) database and included 89 MCI subjects and 80 controls. Voxel-by-voxel differences in gray matter (GM) intensity between the MCI and control groups were identified. Then regions of interest (ROIs) and the most discriminative image features that represented the patterns in MCI subjects were determined for training a classifier. The classifier demonstrated a high classification accuracy (90%) when a behavioral estimate of MCI and the ROIs were included as features in comparison to the behavioral estimate or the ROIs alone, which is one scientific contribution of our work. Another contribution is that the classifier can be integrated with the image processing functions through an online interface with significant medical capability that can be used for automated image pre-processing, obtaining MCI probability estimates for individual cases, and visualization of affected regions.