Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1907
Title: Classification of dementia from FDG-PET parametric images using data mining
Authors: Wen, Lingfeng
Bewley, Michael
Eberl, Stefan
Fulham, Michael J.
Feng, D. David
Subjects: Dementia
Data mining
Classification
Parametric image
Issue Date: 2008
Publisher: IEEE
Source: 2008 5th IEEE International Symposium on Biomedical Imaging : from nano to macro : proceedings : May 14–17, 2008, Paris, France, p. 412-415.
Abstract: It remains a challenge to identify the different types of dementia and separate these from various subtypes from the normal effects of ageing. In this paper the potential of parametric images from FDG-PET studies to aid the classification of dementia using data mining techniques was investigated. Scalar, joint, histogram and voxel-level features were used in the investigation with principal component analysis (PCA) for dimensionality reduction. The logistic regression model and the additive logistic regression model were applied in the classification. The results show that cerebral metabolic rate of glucose consumption (CMRGlc) was efficient in the classification of dementia and data mining using voxel-level features with PCA and the logistic regression model method achieving the best classification.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1907
DOI: 10.1109/ISBI.2008.4541020
ISBN: 978-1-4244-2003-2
Appears in Collections:EIE Conference Papers & Presentations

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