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

Authors:
I. A. Illán;J. M. Górriz;J. Ramírez;D. Salas-Gonzalez;M. M. López;F. Segovia;R. Chaves;M. Gómez-Rio;C. G. Puntonet
Affiliations:
Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Nuclear Medicine Service, Virgen de las Nieves Hospital, Granada, Spain;Department of Computer's Architecture and Technology, University of Granada, 18071 Granada, Spain
Venue:
Information Sciences: an International Journal
Year:
2011

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Abstract

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 ^1^8F-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.