Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images

TLDR

Structural MRI is an effective tool for AD diagnosis, but conventional single‑time‑point approaches are limited by costly nonlinear registration and segmentation, and longitudinal studies, while more sensitive, face computational burdens and inconsistent scan schedules that hinder unified feature extraction. The study proposes a landmark‑based feature extraction method for AD diagnosis from longitudinal MR images that eliminates the need for nonlinear registration or tissue segmentation and remains robust to scan inconsistencies. Discriminative landmarks are automatically learned from training data, rapidly localized in test images without registration or segmentation, and used to derive spatial and longitudinal statistical features that feed a linear SVM to classify AD, MCI, and healthy controls. On the ADNI database, the method achieved 88.30% accuracy for AD versus healthy controls and 79.02% for MCI versus healthy controls, demonstrating superior performance and efficiency.

Abstract

Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.