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, Yong
Wen, Lingfeng
Eberl, Stefan
Fulham, Michael J.
Feng, D. David
Subjects: 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.
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1904
DOI: 10.1109/MMSP.2008.4665057
ISBN: 978-1-4244-2295-1
Appears in Collections:EIE Conference Papers & Presentations

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