NDLI: Automated liver segmentation for whole-body low-contrast CT images from PET-CT scanners

Content Provider	IEEE Xplore Digital Library
Author	Xiuying Wang Changyang Li Eberl, S. Fulham, M. Dagan Feng
Copyright Year	2009
Description	Author affiliation: BMIT Research Group, School of Information Technologies, and Faculty of Medicine, University of Sydney, Australia, and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital (RPAH), Sydney, Australia (Fulham, M.) \|\| BMIT Research Group, School of Information Technologies, University of Sydney, Australia, and Center for Multimedia Signal Processing (CMSP), Dept. of Electronic & Information Engineering, Hong Kong Polytechnic University, and Med-X Research Institute, Shanghai JiaoTong University, China (Dagan Feng) \|\| BMIT Research Group, School of Information Technologies, University of Sydney, Australia, and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital (RPAH), Sydney, Australia (Eberl, S.) \|\| The Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia (Xiuying Wang; Changyang Li)
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 “masking” 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 “masking” 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.
Starting Page	3565
Ending Page	3568
File Size	1009311
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332410
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Computed tomography Information technology Liver neoplasms Positron emission tomography Stomach Lungs Abdomen Biomedical imaging Medical diagnostic imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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