Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1904
Title: Segmentation of dual modality brain PET/CT images using the MAP-MRF model
Authors: Xia, Y
Wen, L
Eberl, S
Fulham, MJ
Feng, DD
Keywords: Markov processes
Brain
Expectation-maximisation algorithm
Image segmentation
Medical image processing
Positron emission tomography
Issue Date: 2008
Publisher: IEEE
Source: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 107-110 How to cite?
Abstract: Dual modality PET/CT has now essentially replaced PET in clinical practice and provided an opportunity to improve image segmentation through the high resolution, lower noise CT data. Thus far most research efforts have concentrated on segmentation of PET-only data. In this work we propose a systematic solution for the automated segmentation of brain PET/CT images into gray, white matter and CSF regions with the MAP-MRF model. Our approach takes advantage of the full information available from the combined scan. A PET/CT image pair and its segmentation result are modelled as a random field triplet, and segmentation is eventually achieved by solving a maximum a posteriori (MAP) problem using the expectation-maximization (EM) algorithm with simulated annealing. We compared the novel algorithm to two widely used PET-only based segmentation methods in the SPM5 toolbox and the VBM toolbox for simulation and patient data. Our results suggest that using the proposed approach substantially improves the accuracy of the delineation of brain structures.
URI: http://hdl.handle.net/10397/1904
ISBN: 978-1-4244-2295-1
DOI: 10.1109/MMSP.2008.4665057
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.
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