Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27500
Title: Automated liver segmentation for whole-body low-contrast CT images from PET-CT scanners
Authors: Wang, X
Li, C
Feng, D
Eberl, S
Fulham, M
Keywords: Cancer
Computerised tomography
Diagnostic radiography
Image segmentation
Liver
Medical image processing
Positron emission tomography
Tumours
Issue Date: 2009
Publisher: IEEE
Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009 : EMBC 2009, 3-6 September 2009, Minneapolis, MN, p. 3565-3568 How to cite?
Abstract: Accurate objective automated liver segmentation in PET-CT studies is important to improve the identification and localization of hepatic tumor. However, this segmentation is an extremely challenging task from the low-contrast CT images captured from PET-CT scanners because of the intensity similarity between liver and adjacent loops of bowel, stomach and muscle. In this paper, we propose a novel automated three-stage liver segmentation technique for PET-CT whole body studies, where: 1) the starting liver slice is automatically localized based on the liver - lung relations; 2) the ldquomaskingrdquo slice containing the biggest liver section is localized using the ratio of liver ROI size to the right half of abdomen ROI size; 3) the liver segmented from the ldquomaskingrdquo slice forms the initial estimation or mask for the automated liver segmentation. Our experimental results from clinical PET-CT studies show that this method can automatically segment the liver for a range of different patients, with consistent objective selection criteria and reproducible accurate results.
URI: http://hdl.handle.net/10397/27500
ISBN: 978-1-4244-3296-7
978-1-4244-3296-7 (E-ISBN)
ISSN: 1557-170X
DOI: 10.1109/IEMBS.2009.5332410
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