GA and adaboost-based feature selection and combination for automated identification of dementia using FDG-PET imaging

Authors:
Yong Xia;Zhe Zhang;Lingfeng Wen;Pei Dong;David Dagan Feng
Affiliations:
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia,Department of PET&Nuclear Medicine, Roya ...;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia,Department of PET&Nuclear Medicine, Roya ...;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia,Med-X Research Institute, Shanghai JiaoTong ...
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

FDG-PET imaging offers the potential for an image-based automated identification of different dementia syndromes. However, various global and local FDG-PET image features have their limitations in characterizing the patterns of this disease. In this paper, we propose an automated approach to identifying the patients with suspected Alzheimer's disease, patients with frontotemporal dementia and normal controls based on the jointly using a group of global features and three groups of local features extracted from parametric FDG-PET images. In this approach, we employ the genetic algorithm to select the features that have best discriminatory ability, and use the AdaBoost technique to adaptively combine four feature groups in constructing a strong classifier. We compared our approach to other classification methods in 154 clinical FDG-PET studies. Our results show that, with the complementary use of the selected global and local features, the proposed approach can substantially improve the accuracy of FDG-PET imaging-based dementia identification.