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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1906

Title: Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging
Authors: Wen, Lingfeng
Eberl, Stefan
Fulham, Michael J.
Feng, D. David
Bai, Jing
Subjects: Monte Carlo methods
Biological tissues
Curve fitting
Fuzzy set theory
Least squares approximations
Medical image processing
Parameter estimation
Pattern clustering
Single photon emission computed tomography
Issue Date: 2008
Publisher: IEEE
Citation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 117-121.
Abstract: Functional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our recently proposed adaptive fuzzy clustering (AFC) technique, which applies standard fuzzy clustering to sub-divided data. Monte Carlo simulations were performed to generate noisy dynamic SPECT data with quantitative analysis for the fitting using the general linear least square method (GLLS) and enhanced model-aided GLLS methods. The results show that AFC substantially improves computational efficiency and obtains improved reliability as standard fuzzy clustering in estimating parametric images but is prone to slight underestimation. Normalization of tissue time activity curves may lead to severe overestimation for small structures when AFC is applied.
Description: DOI: 10.1109/MMSP.2008.4665059
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/10397/1906
ISBN: 978-1-4244-2295-1
Appears in Collections:EIE Conference Papers & Presentations

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