DOI QR코드

DOI QR Code

Extracting The Prostate Boundary Using Direction Features of Prostate Boundary On Ultrasound Prostate Image

  • Park, Jae Heung (Dept. of Computer Science, Gyeongsang Nat'l University) ;
  • Seo, Yeong Geon (Dept. of Computer Science and Graduate School of CCBM, Gyeongsang Nat'l University)
  • Received : 2016.09.24
  • Accepted : 2016.10.26
  • Published : 2016.11.30

Abstract

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. Besides, on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. Accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. In this paper, we propose the method that extracts a prostate boundary using features of its directions on prostate image. As a result of our experiments, it shows that the boundary never falls short of the existing methods or human expert's segmentation. And also, its searching speed is too fast because the method searches a smaller area that other methods.

Keywords

References

  1. 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. 15(6), pp. 859-870, 1996. https://doi.org/10.1109/42.544503
  2. Y. Zhan and D. Shen. "Deformable Segmentation of 3-D Ultrasound Prostate Images Using Statistical Texture Matching Method". IEEE Trans. Med. Imag. 25, pp. 245-255, 2006. https://doi.org/10.1109/TMI.2005.862743
  3. D. Shen, Y. Zhan, and C. Davatzikos. "Segmentation Prostate Boundaries from Ultrasound Images Using Statistical Shape Model". IEEE Trans. Med. Imag. 22(4), pp. 539-551, 2003. https://doi.org/10.1109/TMI.2003.809057
  4. P. Yan, S. Xu, B. Turkbey and J. Kruecker. "Adaptively Learning Local Shape Statistics for Prostate Segmentation Ultrasound". IEEE Trans. On Biomedical Engineering. 58(3), pp. 633-641, 2011. https://doi.org/10.1109/TBME.2010.2094195
  5. H. Akbari, X. Yang, L. V. Halig, and B. Fei. "3D Segmentation of Prostate Ultrasound Images Using Wavelet Transform". Proc. of SPIE, pp. 7962, 2011.
  6. J. Park, "A Prostate Segmentation of TRUS Image using Support Vectors and Snake-like Contour", Journal of The Korea Society of Computer and Information, Vol. 17, No. 12, pp. 101-109, Dec. 2012.
  7. J. Park, "Detecting the Prostate Contour in TRUS Image Using Support Vector Machine and Rotation-invariant Textures", Journal of Digital Content Society, Vol. 15, No. 6, pp. 675-682, Dec. 2014. https://doi.org/10.9728/dcs.2014.15.6.675
  8. S. Kim, "A Prostate Segmentaiton of TRUS Image using Average Shape Model and SIFT Features", KIPS Transaction of Software and Data Engineering, Vol. 1, No. 3, pp. 187-194, Dec. 2012. https://doi.org/10.3745/KTSDE.2012.1.3.187
  9. S. Kim , "A Prostate Segmentation using Gabor Texture Features and Snake-like Contour", Journal of Information Processing System, Vol. 9, No. 1, pp. 103-116, Mar. 2013. https://doi.org/10.3745/JIPS.2013.9.1.103
  10. S. Pelletier and J. R. Cooperstock, "Preconditioning for Edge-Preserving Image Super Resolution", IEEE Transaction on Image Processing, Vol. 21, No. 1, pp. 67-79, Jan. 2012. https://doi.org/10.1109/TIP.2011.2160188