In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.
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