A TRUS prostate segmentation using Gabor texture features and snake-like contour

Journal of Information Processing Systems

Volumn 9, Issue 1, 2013, Pages 103-116

  Kim, Sung Gyun ^a   Seo, Yeong Geon ^a  

^a Gyeongsang National University   (South Korea)

Author keywords

Gabor filter bank; Prostate segmentation; Support vector machines

Indexed keywords

GABOR FEATURE EXTRACTION; GABOR TEXTURE FEATURES; PRE-PROCESSING STEP; PROSTATE SEGMENTATION; ROTATION INVARIANT; SPECKLE REDUCTION; TOP-HAT TRANSFORM; TRANSRECTAL ULTRASOUND IMAGES;

DIAGNOSIS; DISEASES; FILTER BANKS; GABOR FILTERS; IMAGE SEGMENTATION; SPECKLE; UROLOGY;

SUPPORT VECTOR MACHINES;

EID: 84879161988     PISSN: 1976913X     EISSN: 2092805X     Source Type: Journal    
DOI: 10.3745/JIPS.2013.9.1.103     Document Type: Article

References (15)

1. Cancer Facts and Figures. American Cancer Society. http://www.cancer.org
2. Mettlin C: American society national cancer detection project. Cancer 1995, 75:1790-1794.
3. http://www.cancer.org/Research/CancerFactsFigures/CancerFactsFigures/cancer-facts-figures-2011.
4. 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, December, 1996, pp.859-870.
5. P. D. Grimm, J. C. Balsko, and H. Ragde, "Ultrasound guided transperineal implantation of iodine 125 and palladium 103 for the treatment of early stage prostate cancer," Atlas Urol. Clin. No.Amer., Vol.2, 1994, pp.113-125.
6. 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, March, 2006, pp.245-255.
7. A. Rafiee, A. Salimi, and A. Roostam, "A Novel Prostate Segmentation Algorithm in TRUS Images", World Academy of Science, Engineering and Technology 45, 2008, pp.120-124.
8. S. D. Pathak, V. Chalana, D. R. Haynor, and Y. Kim, "Edge-guided boundary delineation in prostate ultrasound images", IEEE Trans. Med. Imag., Vol.19, No.12, December, 2000, pp.1211-1219.
9. 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, April, 2003, pp.539-551.
10. 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, 2003, pp.389-396.
11. P. Yan, S. Xu, B. Turkbey and J. Kruecker, "Adaptively Learning Local Shape Statistics for Prostate Segmentation in US", IEEE Trans. On Biomedical Eng., Vol.58, No.3, March, 2011, pp.633-641.
12. H. Akbari, X. Yang, L. Halig and B. Fei, "3D segmentation of Prostate Ultrasound images Using Wavelet Transform", Proc. of SPIE 7962, 2011.
13. B. E. Boser, I. M. Guyon and V. N. Vapnik."A training algorithm for optimal margin classifiers", In D. Haussler, editor, 5th Annual ACM Workshop on COLT, 1992, pp.144-152.
14. J. Suri and A. Farag, "Deformable Models 2", Springer, 2007, pp.75-94.
15. A. Ghanei, H. S. Zadeh, A. Ratkesicz, and F. Yin, "A three-dimensional deformable model for segmentation of human prostate from ultrasound image," Med. Phys., Vol.28, 2001, pp.2147-2153.