Journal
INFORMATION SCIENCES
Volume 181, Issue 4, Pages 903-916Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.10.027
Keywords
Alzheimer's disease (AD); Computer aided diagnosis; Principal component analysis (PCA); Independent component analysis (ICA); Support vector machine (SVM); Supervised learning; FDG-PET
Categories
Funding
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- NIH [P30 AG010129, K01 AG030514]
- Dana Foundation
- MICINN under the PETRI DENCLASES [PET2006-0253, TEC2008-02113, TEC2007-68030-C02-01, HD2008-0029]
- Consejeria de Innovacion, Ciencia y Empresa (Junta de Andalucia, Spain) [TIC-02566]
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Finding sensitive and appropriate technologies for non-invasive observation and early detection of Alzheimer's disease (AD) is of fundamental importance to develop early treatments. In this work we develop a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline F-18-FDG PET scans from Alzheimer's disease neuroimaging initiative (ADNI) participants. Image projection as feature space dimension reduction technique is combined with an eigenimage based decomposition for feature extraction, and support vector machine (SVM) is used to manage the classification task. A two folded objective is achieved by reaching relevant classification performance complemented with an image analysis support for final decision making. A 88.24% accuracy in identifying mild AD, with 88.64% specificity, and 87.70% sensitivity is obtained. This method also allows the identification of characteristic AD patterns in mild cognitive impairment (MCI) subjects. (C) 2010 Elsevier Inc. All rights reserved.
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