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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1903

Title: Genetic algorithm-based PCA eigenvector selection and weighting for automated identification of dementia using FDG-PET imaging
Authors: Xia, Yong
Wen, Lingfeng
Eberl, Stefan
Fulham, Michael J.
Feng, D. David
Subjects: Algorithms
Alzheimer Disease
Brain
Dementia
Diagnosis, Differential
Fluorodeoxyglucose F18
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Positron-Emission Tomography
Principal Component Analysis
Radiopharmaceuticals
Reproducibility of Results
Sensitivity and Specificity
Issue Date: 2008
Publisher: IEEE
Citation: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : personalized healthcare through technology : August 20-24, 2008, Vancouver, British Columbia, Canada, p. 4812-4815.
Abstract: Parametric FDG-PET data offer the potential for an automated identification of the different dementia syndromes. Principal component analysis (PCA) can be used for feature extraction in FDG-PET. However, standard PCA is not always successful in delineating the features that have the best discrimination ability. We report a genetic algorithm-based method to identify an optimal combination of eigenvectors so that the resultant features are capable of successfully separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. We compared our approach with standard PCA on a set of 210 clinical cases and improved the performance in separating the dementia types with an accuracy of 90.0% and a Kappa statistic of 0.849. There was very good agreement between the automated technique and the diagnosis given by clinicians.
Description: DOI: 10.1109/IEMBS.2008.4650290
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1903
ISBN: 978-1-4244-1815-2
Appears in Collections:EIE Conference Papers & Presentations

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