Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images

Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)


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.

Original languageEnglish
Article number7929275
Pages (from-to)1607-1616
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number6
Publication statusPublished - 2017 Nov


  • Alzheimer's disease
  • landmark-based feature extraction
  • longitudinal study
  • structural magnetic resonance imaging

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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