Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20044
Title: Fully automated liver segmentation for low- and high- contrast CT volumes based on probabilistic atlases
Authors: Li, C
Wang, X
Eberl, S
Fulham, M
Yin, Y
Feng, D
Keywords: Computed tomography
Liver segmentation
Probabilistic atlas
Issue Date: 2010
Publisher: IEEE
Source: 2010 17th IEEE International Conference on Image Processing (ICIP), 26-29 September 2010, Hong Kong, p. 1733-1736 How to cite?
Abstract: Automated liver segmentation is problematic due to variations in liver shape / size and because the liver has a similar density distribution to surrounding structures. We propose a method that: 1) utilizes iteratively constructed probabilistic liver and rib cage atlases, 2) conducts the Gaussian distribution analysis to avoid incorrectly classifying the irrelevant surrounding tissues as `liver region' in the conventional probabilistic atlas based method, and maps the intensity range of the input candidate liver region onto the liver atlas, 3) retrieves the `missing parts' of the liver by deformable registration. Our approach is automated and able to segment the liver from high-contrast and low-contrast CT volumes. Forty clinical CT studies were used for atlas construction and validation. Our method outperformed two other probabilistic atlas-based liver segmentation methods.
URI: http://hdl.handle.net/10397/20044
ISBN: 978-1-4244-7992-4
978-1-4244-7993-1 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5654434
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

8
Citations as of Feb 25, 2017

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
0
Citations as of Aug 15, 2017

Page view(s)

30
Last Week
1
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.