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Proceedings Paper

Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia
Author(s): Ahmed Serag; Fabian Wenzel; Frank Thiele; Ralph Buchert; Stewart Young
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Paper 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.

Paper Details

Date Published: 3 March 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726012 (3 March 2009); doi: 10.1117/12.811562
Show Author Affiliations
Ahmed Serag, Univ. zu Lübeck (Germany)
Philips Research (Germany)
Fabian Wenzel, Philips Research (Germany)
Frank Thiele, Philips Research (United States)
Ralph Buchert, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Stewart Young, Philips Research (Germany)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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