Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

Ahmed Serag*, Fabian Wenzel, Frank Thiele, Ralph Buchert, Stewart Young

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

FDG-PET is increasingly used for the evaluation of dementia patients, as major neurodegenerative disorders, such as Alzheimer's disease (AD), Lewy body dementia (LBD), and Frontotemporal dementia (FTD), have been shown to induce specific patterns of regional hypo-metabolism. However, the interpretation of FDG-PET images of patients with suspected dementia is not straightforward, since patients are imaged at different stages of progression of neurodegenerative disease, and the indications of reduced metabolism due to neurodegenerative disease appear slowly over time. Furthermore, different diseases can cause rather similar patterns of hypo-metabolism. Therefore, classification of FDG-PET images of patients with suspected dementia may lead to misdiagnosis. This work aims to find an optimal subset of features for automated classification, in order to improve classification accuracy of FDG-PET images in patients with suspected dementia. A novel feature selection method is proposed, and performance is compared to existing methods. The proposed approach adopts a combination of balanced class distributions and feature selection methods. This is demonstrated to provide high classification accuracy for classification of FDG-PET brain images of normal controls and dementia patients, comparable with alternative approaches, and provides a compact set of features selected.

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7260
DOIs
Publication statusPublished - 15 Jun 2009
EventMedical Imaging 2009: Computer-Aided Diagnosis - Lake Buena Vista, FL, United Kingdom
Duration: 10 Feb 200912 Feb 2009

Conference

ConferenceMedical Imaging 2009: Computer-Aided Diagnosis
CountryUnited Kingdom
CityLake Buena Vista, FL
Period10/02/0912/02/09

Keywords

  • Automated classification
  • Dementia
  • FDG-PET
  • Feature selection
  • Human brain
  • Pattern recognition

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