Chapter

Intelligent Science and Intelligent Data Engineering

Volume 7202 of the series Lecture Notes in Computer Science pp 128-135

GA and AdaBoost-Based Feature Selection and Combination for Automated Identification of Dementia Using FDG-PET Imaging

  • Yong XiaAffiliated withBiomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of SydneyDepartment of PET&Nuclear Medicine, Royal Prince Alfred Hospital
  • , Zhe ZhangAffiliated withBiomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney
  • , Lingfeng WenAffiliated withBiomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of SydneyDepartment of PET&Nuclear Medicine, Royal Prince Alfred Hospital
  • , Pei DongAffiliated withBiomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney
  • , David Dagan FengAffiliated withBiomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of SydneyMed-X Research Institute, Shanghai JiaoTong University

* Final gross prices may vary according to local VAT.

Get Access

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.

Keywords

FDG-PET imaging dementia classification genetic algorithm AdaBoost algorithm support vector machine