title: Automated identification of dementia using FDG-PET imaging.
abstract: 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.
authors.affiliation: Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China ; Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia.
authors.forename: Yong
authors.identifier: 0000-0002-8427-4935
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Shen
authors.identifier: 0000-0001-9375-380X
authors.initials: S
authors.lastname: Lu
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia.
authors.forename: Lingfeng
authors.initials: L
authors.lastname: Wen
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia.
authors.forename: Stefan
authors.identifier: 0000-0001-6069-7530
authors.initials: S
authors.lastname: Eberl
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia ; Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Fulham
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D019992:Databases as Topic; D003704:Dementia / Q000000981:diagnostic imaging*; D019788:Fluorodeoxyglucose F18; D006801:Humans; D049268:Positron-Emission Tomography
chemical_list: D019788:Fluorodeoxyglucose F18