Compartmental sparse feature selection method for Alzheimer's disease identification.

Abstract

For high-dimensional magnetic resonance imaging (MRI) data, many feature selection methods have been proposed to reduce feature dimension in the study of computer-aided Alzheimer's disease (AD) diagnosis. This paper presents a compartmental sparse feature selection method used for AD identification. Based on the derived atlas-based regions-of-interest (ROIs) of brain, the proposed method partitioned the T1-weighted MRI data into several compartments. It performs feature selection and classification compartmentally according to the local feature dimension estimation and local feature selection using sparse principal component analysis (SPCA) method followed with elastic-net logistic regression (ENLR) classifier. Experimental results showed that the proposed method improves the classification performance for small ROIs with high computational efficiency.