18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis

• Published in: Information sciences Vol. 181; no. 4; pp. 903 - 916
• Main Authors: Illán, I.A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M.M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.G.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.02.2011
• Subjects: Alzheimer's disease; Alzheimer’s disease (AD); Classification; Computer aided diagnosis; Diagnosis; FDG-PET; Feature extraction; Imaging; Impairment; Independent component analysis (ICA); Polyethylene terephthalates; Principal component analysis (PCA); Supervised learning; Support vector machine (SVM); Support vector machines; FDG-PET; Support vector machine (SVM); Alzheimer’s disease (AD); Supervised learning; Computer aided diagnosis; Independent component analysis (ICA); Principal component analysis (PCA)
• ISSN: 0020-0255
• DOI: 10.1016/j.ins.2010.10.027

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 18F-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.