Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341T (21 March 2014); doi: 10.1117/12.2042959

Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)