Browse > Article
http://dx.doi.org/10.3745/KTSDE.2014.3.5.187

An Average Shape Model for Segmenting Prostate Boundary of TRUS Prostate Image  

Kim, Sang Bog (경상대학교 컴퓨터과학과)
Chung, Joo Young (경상대학교 컴퓨터과학과)
Seo, Yeong Geon (경상대학교 컴퓨터과학과 대학원 문화융복합학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.3, no.5, 2014 , pp. 187-194 More about this Journal
Abstract
Prostate cancer is a malignant tumor occurring in the prostate. Recently, the repetition rate is increasing. Image inspection method which we can check the prostate structure the most correctly is MRI(Magnetic Resonance Imaging), but it is hard to apply it to all the patients because of the cost. So, they use mostly TRUS(Transrectal Ultrasound) images acquired from prostate ultrasound inspection and which are cheap and easy to inspect the prostate in the process of treating and diagnosing the prostate cancer. Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. In this study, we propose an average shape model to segment the prostate boundary in TRUS prostate image. The method has 3 steps. First, it finds the probe using edge distribution. Next, it finds two straight lines connected with the probe. Finally it puts the shape model to the image using the position of the probe and straight lines.
Keywords
TRUS; Prostate; Prostate Cancer; Average Shape Model; Boundary Segmentation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Zhan and D. Shen, "Deformable Segmentation of 3-D Ultrasound Prostate Images Using Statistical Texture Matching Method", IEEE Trans. on Medical Imaging, Vol.25, pp.245-255, March, 2006.   DOI   ScienceOn
2 A. Rafiee, A. Salimi, and A. Roostam, "A Novel Prostate Segmentation Algorithm in TRUS Images", World Academy of Science, Engineering and Technology 45, pp.120-124, 2008.
3 S. D. Pathak, V. Chalana, D. R. Haynor, and Y. Kim, "Edge-guided Boundary Delineation in Prostate Ultrasound Images", IEEE Trans. on Medical Imaging, Vol.19, No.12, pp.1211-1219, Dec., 2000.   DOI   ScienceOn
4 D. Shen, Y. Zhan, and C. Davatzikos, "Segmentation Prostate Boundaries from Ultrasound Images Using Statistical Shape Model", IEEE Trans. on Medical. Imaging, Vol.22, No.4, pp.539-551, Apr., 2003.   DOI   ScienceOn
5 F. Shao, K. V. Ling, and W. S. Ng, "3-D Prostate Surface Detection from Ultrasound Images Based on Level Set Method", Proc. MICCAI 2003, pp.389-396, 2003.
6 P. Yan, S. Xu, B. Turkbey and J. Kruecker, "Adaptively Learning Local Shape Statistics for Prostate Segmentationin Ultrasound", IEEE Trans. On Biomedical Engineering, Vol.58, No.3, pp.633-641, Mar., 2011.   DOI
7 H. Akbari, X. Yang, L. Halig and B. Fei, "3D Segmentation of Prostate Ultrasound Images Using Wavelet Transform", Proc. of SPIE 7962, 2011.
8 Jong M. Park, Chang L. Yoon, and Sung G. Lee, "Survey about the Method of Image Segmentation", KIICS, Vol.21, No.1, pp.255-258, 1994.
9 Betrouni, N, Puech, P., Dewalle, A.S., Vermandel. M, and Rousseau. J., "3D delineation of prostate, rectum and bladder on MI images", Computerized Medical Imaging and Graphics 32, pp.662-630, 2007.
10 Klein, S. and etc, "Segmentation of the Prostate in MR Images by Atlas Matching", Biomedical Imaging, ISBI 2007. 4th IEEE International Symposium, pp.410-413, 2007.
11 Betrouni, N and etc, "3D Automatic Segmentation and Reconstruction of Prostate on MR Images", IEEE Engineering in medicine and biology society, pp.5259-5262, 2007.
12 Yanong S., Stuart W., and Peyer Z., "A Hybrid ASM Approach for Sparse Volumetric Data Segmentation", Pattern Recognition and Image Analysis, Vol.17, No.2, pp.252-258, 2007.   DOI
13 Cancer Facts and Figures. American Cancer Society [Internet]. http://www.cancer.org.
14 Mettlin C: American society national cancer detection project. Cancer, pp.1790-1794, 1995.
15 [internet] http://www.cancer.go.kr
16 A. Chakraborty, L. H. Staib, and J. S. Duncan, "Deformable Boundary Finding in Medical Images by Integrating Gradient and Region Information", IEEE Trans. on Medical Imaging., Vol.15, No.6, pp.859-870, Dec., 1996.   DOI   ScienceOn
17 P. D. Grimm, J. C. Balsko, and H. Ragde, "Ultrasound Guided Transperineal Implantation of Iodine 125 and Palladium 103 for the Treatment to Fearly Stage Prostate Cancer", Atlas Urol. Clin. no. Amer., Vol.2, pp.113-125, 1994.