Full Text:   <2244>

CLC number: TP391.4

On-line Access: 

Received: 2008-03-06

Revision Accepted: 2008-06-01

Crosschecked: 2008-12-26

Cited: 9

Clicked: 3518

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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

http://doi.org/10.1631/jzus.A0820157


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",
volume="10",
number="2",
pages="239~246",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820157"
}

%0 Journal Article
%T Automatic segmentation of bladder in CT images
%A Feng SHI
%A Jie YANG
%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

TY - JOUR
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


Abstract: 
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

Reference

[1] Armato, S.G.III, Giger, M.L., Moran, C.J., Blackburn, J.T., Doi, K., MacMahon H., 1999. Computerized detection of pulmonary nodules on CT scans. RadioGraphics, 19:1303-1311.

[2] Bueno, G., Martínez-Albalá, A., Adan, A., 2004. Fuzzy-snake Segmentation of Anatomical Structures Applied to CT Images. Int. Conf. on Image Analysis and Recognition, 2:33-42.

[3] Camapum, J.F., Silva, A.O., Freitas, A.N., Bassani, H.F., Freitas, F.M.O., 2004. Segmentation of Clinical Structures from Images of the Human Pelvic Area. Proc. 17th Brazilian Symp. on Computer Graphics and Image Processing, p.10-16.

[4] Cheng, Y., 1995. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell., 17(8):790-799.

[5] Comaniciu, D., Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603-619.

[6] Costa, M.J., Delingette, H., Novellas, S., Ayache, N., 2007. Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models. 10th Int. Conf. on Medical Image Computing and Computer-assisted Intervention, p.252-260.

[7] Freedman, D., Zhang, T., 2005. Interactive Graph Cut Based Segmentation with Shape Priors. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1:755-762.

[8] Fukunaga, K., Hostetler, L., 1975. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory, 21(1):32-40.

[9] Georgescu, B., Shimshoni, I., Meer, P., 2003. Mean Shift Based Clustering in High Dimensions: A Texture Classification Example. Proc. Ninth IEEE Int. Conf. on Computer Vision, 1:456-463.

[10] Haas, B., Coradi, T., Scholz, M., Kunz, P., Huber, M., Oppitz, U., Andre, L., Lengkeek, V., Huyskens, D., van Esch, A., et al., 2008. Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies. Phys. Med. Biol., 53(6):1751-1771.

[11] Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J., 1993. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell., 15(9):850-863.

[12] Jimenez, J.R., Medina, V., Yanez, O., 2003. Nonparametric MRI Segmentation Using Mean Shift and Edge Confidence Maps. Proc. SPIE, 5032:1433-1441.

[13] Lee, C.C., Chung, P.C., 2004. Identifying Abdominal Organs Using Robust Fuzzy Inference Model. IEEE Int. Conf. on Networking, Sensing and Control, 2:1289-1294.

[14] Li, J., Fang, X., Hou, J., 2007. Mean shift based log-Gabor wavelet image coding. J. Zhejiang Univ. Sci. A, 8(4):620-624.

[15] Mayer, A., Greenspan, H., 2006. Segmentation of Brain MRI by Adaptive Mean Shift. 3rd IEEE Int. Symp. on Biomedical Imaging: Nano to Macro, p.319-322.

[16] Mazonakis, M., Damilakis, J., Varveris, H., Prassopoulos, P., Gourtsoyiannis, N., 2001. Image segmentation in treatment planning for prostate cancer using the region growing technique. Br. J. Radiol., 74:243-248.

[17] Rousson, M., Khamene, A., Diallo, M., Celi, J.C., Sauer, F., 2005. Constrained Surface Evolutions for Prostate and Bladder Segmentation in CT Images. First Int. Workshop on Computer Vision for Biomedical Image Applications, p.251-260.

[18] Xu, W., Amin, S.A., Haas, O.C.L., Burnham, K.J., Mills, J.A., 2003. Contour Detection by Using Radial Searching for CT Images. 4th Int. IEEE EMBS Special Topic Conf. on Information Technology Applications in Biomedicine, p.346-349.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE