Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Automatic Prostate Segmentation from Ultrasound Images using Morphological Features

형태학적 특징을 이용한 초음파 영상에서의 자동 전립선 분할

  • Kim, Kwang Baek (Department of Artificial Intelligence, Silla University)
  • Received : 2022.05.24
  • Accepted : 2022.06.07
  • Published : 2022.06.30
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Abstract

In this paper, we propose a method of extracting prostate region using morphological characteristics of ultra-sonic image of prostate. In the first step of the proposed method, the edge area of the prostate image is extracted. The histogram of ultra-sonic image is used to extract base objects to detect the upper edge of prostate region by altering the contrast of the image, then, the lower edges of the extracted base objects are connected by using monotone cubic spline interpolation to extract the upper edge. Step 2, Otsu's binarization is applied to the region under the extracted upper edge of the prostate ultra-sonic image to extract the lower edge of prostate. In the last step, the upper and the lower edges are connected to extract prostate region and by comparing the extracted region of prostate with the one measured manually, the result showed that the morphological characteristics of prostate in ultrasonic image can be utilized to extract the prostate region.

본 논문에서는 전립선 초음파 영상에서 형태학적 특징을 이용하여 전립선 영역을 검출하는 방법을 제안한다. 제안된 방법의 첫 단계에서는 전립선 영역의 상단 경계선을 추출한다. 초음파 촬영으로 획득한 영상에서 히스토그램 정보를 이용해 명암대비를 조정하여 전립선 영역의 상단 경계선을 검출하기 위한 기준 객체들을 추출하고, 기준 객체들의 하단 경계선을 Monotone cubic spline 보간법을 적용하여 상단 경계선을 추출한다. 두 번째 단계에서는 전립선 초음파 영상에서 추출한 상단 경계선보다 아래에 위치한 영역에 대해 오츠 이진화를 적용하여 전립선 하단 경계선을 추출한다. 마지막으로 전립선 상단 경계선과 하단 경계선을 연결하여 전립선 영역을 추출한다. 수동으로 측정한 전립선 영역과 비교 분석한 결과, 전립선 초음파 영상이 갖는 형태학적 특징을 이용한 방법으로 전립선 영역을 추출할 수 있는 것을 확인하였다.

Keywords

References

  1. Y. Yu, Y. Chen, and B. Chiu, "Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation," Computers in biology and medicine, vol. 74, no. 1, pp. 74-90, Jul. 2016. https://doi.org/10.1016/j.compbiomed.2016.05.002
  2. K. W. Jung, Y. J. Won, C. M. Oh, H. J. Kong, D. H. Lee, and K. H. Lee, "Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2014," Cancer Research and Treatment, vol. 49, no. 2, pp. 292-305, Mar. 2017. https://doi.org/10.4143/crt.2017.118
  3. H. K. Park, H. Park, J. Bae, S. K. Hong, S. -S. Byun, S. E. Lee, K. W. Kim, S. Y. Cho, S. J. Jeong, and C. Y. Yoon, "The Prevalence of Benign Prostatic Hyperplasia in Elderly Men in Korea: A Community-Based Study," Korean Journal of Urology, vol. 50, no. 9, pp. 843-847, Sep. 2009. https://doi.org/10.4111/kju.2009.50.9.843
  4. W. Park, "Diagnosis and management of prostate cancer," Journal of the Korean Medical Association, vol. 58. no. 1, pp. 5-6, Jan. 2015. https://doi.org/10.5124/jkma.2015.58.1.5
  5. J. A. Noble and D. Boukerroui, "Ultrasound image segmentation: a survey," IEEE Transactions on medical imaging, vol. 25, no. 8, pp. 987-1010, Aug. 2006. https://doi.org/10.1109/TMI.2006.877092
  6. P. Yan, S. Xu, B. Turkbey, and J. Kruecker, "Discrete deformable model guided by partial active shape model for TRUS image segmentation," IEEE Transactions on Biomedical Engineering, vol. 57, no. 5, pp. 1158-1166, May. 2010. https://doi.org/10.1109/TBME.2009.2037491
  7. F. A. Cosio, "Automatic Initialization of an Active Shape Model of the Prostate," Medical Image Analysis, vol. 12, no. 4, pp. 469-483, Aug. 2008. https://doi.org/10.1016/j.media.2008.02.001
  8. F. Sahba, H. R. Tizhoosh, and M. M. A. Salama, "Segmentation of prostate boundaries using regional contrast enhancement," in Proceedings of IEEE International Conference on Image Processing, Genova, Italy, vol. 2, pp. 1266-1269, Nov. 2005.
  9. N. N. Kachouie, P. Fieguth, and S. Rahnamayan, "An elliptical level set method for automatic TRUS prostate image segmentation," in Proceedings of IEEE International Symposium on Signal Processing and Information Technology, Vancouver: BC, Canada, pp. 191-196, Aug. 2006.
  10. K. B. Kim, H. J. Park, D. H. Song, and B. K. Choi, "Automatic Ultrasonographic Measurement of Abdominal Muscle Thickness with Fuzzy Binarization and Image Processing Techniques," Journal of Medical Imaging and Health Informatics, vol. 6, no. 6, pp. 1363-1369, Oct. 2016. https://doi.org/10.1166/jmihi.2016.1813
  11. H. M. Ladak, F. Mao, Y. Wang, D. B. Downey, D. A. Steinman, and A. Fenster, "Prostate segmentation from 2D ultrasound images," in Proceedings of the 22nd Annual International Conference the IEEE Engineering in Medicine and Biology Society, Chicago: IL, USA, vol. 4, pp. 3188-3191, Jul. 2000.
  12. N. Betrouni, M. Vermandel, D. Pasquier, S. Maouche, and J. Rousseau, "Segmentation of Abdominal Ultrasound Images of the Prostate Using A priori Information and an Adapted Noise Filter," Computerized Medical Imaging and Graphics, vol. 29, no. 1, pp. 43-51, Jan. 2005. https://doi.org/10.1016/j.compmedimag.2004.07.007
  13. S. Badiei, S. E. Salcudean, J. Varah and W. J. Morris, "Prostate segmentation in 2D ultrasound images using image warping and ellipse fitting," in Proceedings of Medical Image Computing and Computer-Assisted Intervention-MICCAI, Copenhagen, Denmark, vol. 4191, pp. 17-24, Oct. 2006.