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http://dx.doi.org/10.9708/jksci/2012.17.12.101

A ProstateSegmentationofTRUS ImageusingSupport VectorsandSnake-likeContour  

Park, Jae Heung (Dept. of Computer Science, Gyeongsang National Univ.)
Se, Yeong Geon (Dept. of Computer Science, Gyeongsang National Univ.)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.12, 2012 , pp. 101-109 More about this Journal
Abstract
In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation inTRUS images using support vectors and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. Gabor filter bank for extracting the texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. The boundary of prostate is extracted by the snake-like contour algorithm. The results showed that this new algorithm extracted the prostate boundary with less than 9.3% relative to boundary provided manually by experts.
Keywords
Support Vector; Snake-like Contour; TRUS; Prostate;
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  • Reference
1 Cancer Facts and Figures.American Cancer Society.
2 Mettlin C: American society nationalcancer detectionproject.Cancer1995,75:1790-1794.   DOI
3 A. Chakraborty, L. H. Staib, and J. S. Duncan,"Deformable Boundary Finding in Medical Images by Integrating Gradient and Region Information," IEEE Trans. Med. Imag., Vol.15, No.6, pp.859-870, Dec. 1996.   DOI   ScienceOn
4 Y. Zhanand D. Shen, "Deformable Segmentation of 3-D US Prostate Images Using Statistical Texture Matching Method," IEEE Trans. on Med. Imag., Vol.256, pp. 245-255, March 200
5 A. Rafiee, A. Salimi, and A. Roostam, "A Novel Prostate Segmentation Algorithm in TRUS Images," World Academy of Science, Eng. and Tech. 45, pp.120-124, 2008.
6 S. D. Pathak, V. Chalana, D. R. Haynor, and Y. Kim, "Edge-guided Boundary Delineation in Prostate US Images," IEEE Trans. Med. Imag., Vol.19, No.12, pp.1211-1221, Dec. 2009.
7 D. Shen, Y. Zhan, and C. Davatzikos, "Segmentation Prostate Boundaries from Ultrasound Images Using Statistical Shape Model," IEEE Trans. Med. Imag., Vol.22, No.4, pp.539-551, Apr. 2003.   DOI   ScienceOn
8 F. Shao, K. V. Ling, and W. S. Ng, "3-D Prostate Surface Detection from Ultrasound Images based on Level Set Method," in Proc. MICCAI 2003, pp.389-396, 2003.
9 http://www.cancer.org/Research/CancerFactsFigures/CancerFactsFigures/cancer-facts-figures-2011
10 P. D. Grimm, J. C. Balsko, and H. Ragde, "US Guided Transperineal Implantation of Iodine 125 and Palladium 103 for theTreatment of Early Stage Prostate Cancer," Atlas Urol. Clin. No. Amer., Vol.2, pp.113-125, 1994.
11 J. Suriand A. Farag, "Deformable Models2," Springer, pp.75-94, 2007.
12 P. Yan, S. Xu, B. Turkbey and J. Kruecker, "Adaptively Learning Local Shape Statistics for Prostate Segmentation in Ultrasound," IEEE Trans. On Bio. Eng., Vol. 58, No. 3, pp. 633-641, Mar. 2011.   DOI   ScienceOn
13 H. Akbari, X. Yang, L. Haligand B. Fei, "3D Segmentation of Prostate US Images Using Wavelet Transform," Proc. of SPIE, 2011.
14 B. E. Boser, I. M. Guyonand V. N. Vapnik. "A Training Algorithm for Optimal Margin Classifiers," In D. Haussler, 5th Annual ACM Workshopon COLT, pp.144-152,1992.
15 A. Ghanei, H. Zadeh, A. Ratkesicz, and F. Yin, "A 3D Deformable Model for Segmentation of Human Prostatefrom Ultrasound Image," Med. Phys., Vol.28, pp.2147-2153. 2001.   DOI   ScienceOn