A Novel Prostate Segmentation Algorithm in TRUS Images

Authors: Ali Rafiee, Ahad Salimi, Ali Reza Roosta

Abstract:

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. 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 novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.

Keywords: Prostate segmentation, stick filter, neural network, active contour.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327883

References:

[1] Cancer Facts and Figures. American Cancer Society. (Online) http://www.cancer.org, 2002.
[2] Mettlin C: American society national cancer detection project. Cancer 1995, 75:1790-1794.
[3] Sayan D. Pathak, Vikram Chalana, David R. Haynor, and Yongmin Kim.Edge-guided boundary delineation in prostate ultrasound images.IEEE Trans. Med. Imaging, 19(12):1211-1219, 2000.
[4] J. M. Fitzpatrick and J. M. Reinhardt, editors.Prostate ultrasound image segmentation using level set-based region flow with shapeguidance. SPIE, Apr. 2005.
[5] Ahmed Jendoubi, Jianchao Zeng, and Mohamed F. Chouikha.Top-down approach to segmentation of prostate boundaries in ultrasound images.In AIPR, pages 145-149, 2004.
[6] Farhang Sahba, Hamid R. Tizhoosh, and Magdy M.A. Salama.Segmentation of prostate boundaries using regional contrast enhancement. In the IEEE International Conference on Image Processing (ICIP), volume 2, pages1266-1269, Sept. 2005.
[7] C.K. Kwoh, M. Teo, W. Ng, S. Tan, and M. Jones "Outlining the prostate boundary using the harmonics method," Med. Biol. Eng. Computing, vol. 36, pp. 768-771, 1998.
[8] R.G. Aarnink, R.J.B. Giesen, A. L. Huynen, J. J. de la Rosette, F.M. Debruyne, and H. Wijkstra, "A practical clinical method for contour determination in ultrasound prostate images," Ultrasound Med. Biol., vol. 20, pp. 705-717, 1994.
[9] R.G. Aarnink, S.D. Pathak, J. J. de la Rosette, F.M. Debruyne, Y. Kim and H. Wijkstra, "Edge detection in ultrasound prostate images using integrated edge map.," Ultrasound Med. Biol., vol. 36, pp. 635-642, 1998.
[10] Y. Zhan and D. Shen, "Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method," IEEE Transactions on Medical Imaging, vol. 25, pp. 256-272, 2006.
[11] D. Freedman, R.J. Radke, T. Zhang, Y. Jeong, D.M. Lovelock, and G.T.Y. Chen, "Model-based segmentation of medical imagery by matching distributions," IEEE Transactions on Medical Imaging, vol. 24, pp. 281-292, 2005.
[12] A. Ghanei, H. Soltanian-Zadeh, A. Ratkewicz, and F. Yin, "A three dimentional deformable model for segmentation of human prostate from ultrasound images," Med. Phys, vol. 28, pp. 2147-2153, 2001.
[13] C. Knoll, M. Alcaniz, V. Grau, C. Monserrat, and M. Juan, "Outlining of the prostate using snakes with shape restrictions based on the wavelet transform," Pattern Recognition, vol. 32, pp. 1767-1781,1999.
[14] S. D. Pathak, V. Chalana, D. haynor, and Y. kim, "Edge guided boundary delineationin prostate ultrasound images,"IEEE Transactions on Medical Imaging, vol. 19, pp. 1211-1219, 2000.
[15] R.C. Gonzalez and R.E. Woods.Digital Image Processing, 2nd. Ed.Prentice-Hall, 2002.
[16] R.C. Gonzalez and R.E. Woods.Digital Image Processingusing matlab. Ed.Prentice-Hall, 2004.
[17] Michael Kass, Andrew Witkin, and Demetri Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, pages 321-331, 1988.