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CLC number: TP391.4

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Received: 2008-03-06

Revision Accepted: 2008-06-01

Crosschecked: 2008-12-26

Cited: 9

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.2 P.239-246


Automatic segmentation of bladder in CT images

Author(s):  Feng SHI, Jie YANG, Yue-min ZHU

Affiliation(s):  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   shifeng.sjtu@gmail.com

Key Words:  Image segmentation, Computerized tomography (CT), Mean shift, Bladder, Rolling ball

Feng SHI, Jie YANG, Yue-min ZHU. Automatic segmentation of bladder in CT images[J]. Journal of Zhejiang University Science A, 2009, 10(2): 239-246.

@article{title="Automatic segmentation of bladder in CT images",
author="Feng SHI, Jie YANG, Yue-min ZHU",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Automatic segmentation of bladder in CT images
%A Feng SHI
%A Yue-min ZHU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 2
%P 239-246
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820157

T1 - Automatic segmentation of bladder in CT images
A1 - Feng SHI
A1 - Jie YANG
A1 - Yue-min ZHU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 2
SP - 239
EP - 246
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820157

Segmentation of the bladder in computerized tomography (CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorff distance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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