Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014, Article ID 421743, 8 pages
Research Article

Automated Identification of Dementia Using FDG-PET Imaging

1Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
3Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
4Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
5Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China

Received 30 April 2013; Revised 1 October 2013; Accepted 17 November 2013; Published 2 February 2014

Academic Editor: Annalena Venneri

Copyright © 2014 Yong Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Parametric FDG-PET images offer the potential for automated identification of the different dementia syndromes. However, various existing image features and classifiers have their limitations in characterizing and differentiating the patterns of this disease. We reported a hybrid feature extraction, selection, and classification approach, namely, the GA-MKL algorithm, for separating patients with suspected Alzheimer’s disease and frontotemporal dementia from normal controls. In this approach, we extracted three groups of features to describe the average level, spatial variation, and asymmetry of glucose metabolic rates in 116 cortical volumes. An optimal combination of features, that is, capable of classifying dementia cases was identified by a genetic algorithm- (GA-) based method. The condition of each FDG-PET study was predicted by applying the selected features to a multikernel learning (MKL) machine, in which the weighting parameter of each kernel function can be automatically estimated. We compared our approach to two state-of-the-art dementia identification algorithms on a set of 129 clinical cases and improved the performance in separating the dementia types, achieving accuracy of 94.62%. There is a very good agreement between the proposed automated technique and the diagnosis made by clinicians.