TY - JOUR
T1 - Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images
AU - Zhang, Jun
AU - Liu, Mingxia
AU - An, Le
AU - Gao, Yaozong
AU - Shen, Dinggang
N1 - Funding Information:
Manuscript received January 3, 2017; revised April 6, 2017; accepted May 10, 2017. Date of publication May 16, 2017; date of current version November 3, 2017. This work was supported by the National Institutes of Health under Grant EB006733, Grant EB008374, Grant EB009634, Grant MH100217, Grant AG041721, Grant AG049371, and Grant AG042599. (Corresponding author: Dinggang Shen.) J. Zhang, M. Liu, and L. An are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27514 USA (e-mail: xdzhangjun@gmail.com; mingxia_liu@med.unc.edu; le_an@ med.unc.edu).
Publisher Copyright:
© 2013 IEEE.
PY - 2017/11
Y1 - 2017/11
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - landmark-based feature extraction
KW - longitudinal study
KW - structural magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85030163432&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2017.2704614
DO - 10.1109/JBHI.2017.2704614
M3 - Article
C2 - 28534798
AN - SCOPUS:85030163432
SN - 2168-2194
VL - 21
SP - 1607
EP - 1616
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 6
M1 - 7929275
ER -