Region-of-Interest based sparse feature learning method for Alzheimer's disease identification.

•Reasonable feature dimension is estimated while protect local features with high computational efficiency.•The extracted feature parameters describe well the atrophy of featured ROIs of the brain as clinical parameters but show better performance in AD identification than clinical parameters.•Feature extraction and classification are performed compartmentally and automatically instead of extracting clinical features preliminarily.