Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Prostate Object Extraction in Ultrasound Volume Using Wavelet Transform

초음파 볼륨에서 웨이브렛 변환을 이용한 전립선 객체 추출

  • Oh Jong-Hwan (Telecommunication Network, Samsung Electronics) ;
  • Kim Sang-Hyun (Department of Multimedia Engineering, Youngsan University) ;
  • Kim Nam-Chul (Department of Electronic Engineering, Kyungpook National University)
  • 오종환 ((주)삼성전자 정보통신총괄) ;
  • 김상현 (영산대학교 멀티미디어공학부) ;
  • 김남철 (경북대학교 전자공학과)
  • Published : 2006.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

This thesis proposes an effi챠ent method for extracting a prostate volume from 3D ultrasound image by using wavelet transform and SVM classification. In the proposed method, a modulus image for each 2D slice is generated by averaging detail images of horizontal and vertical orientations at several scales, which has the sharpest local maxima and the lowest noise power compared to those of all single scales. Prostate contour vertices are determined accurately using a SVM classifier, where feature vectors are composed of intensity and texture moments investigated along radial lines. Experimental results show that the proposed method yields absolute mean distance of on average 1.89 pixels when the contours obtained manually by an expert are used as reference data.

본 논문에서는 웨이브렛 변환과 SVM 분류기를 이용하여 3차원 초음파 볼륨으로부터 전립선 객체를 추출하는 방법을 제안한다. 제안한 방법에서는 웨이브렛 변환의 수평 수직 방향의 상세 영상들의 평균치들로부터 웨이브렛 변환 모듈러스 영상을 구함으로써 잡음전력 대비 전립선 윤곽에 대한 국부 최대치들의 첨예도가 큰 모듈러스 영상을 얻을 수 있다. 또한 전립선의 밝기 변이 특성 및 전립선 내외부의 질감 차이 등을 특징으로 한 SVM 분류기를 이용함으로써 전립선 윤곽 추출의 정확도를 크게 향상시킬 수 있다. 실험 결과, 제안한 방법을 이용하여 전립선 윤곽을 찾을 경우 전문가에 의하여 추출된 윤곽과 비교하여 절대 평균 거리가 1.89로 나타났다.

Keywords

References

  1. American cancer society homepage [Online]. Available: http://www.cancer.org
  2. A. Fenster, S. Tong, H. N. Cardinal, C. Blake, and D. B. Downey, 'Three-dimensional ultrasound imaging system for prostate cancer diagnosis and treatment,' IEEE Trans. Instrum. and Measurem., vol. 47, pp. 1439-1447, Dec. 1998 https://doi.org/10.1109/19.746709
  3. L. Gong, S. D. Pathak, D. H. Haynor and Y. Kim, 'Parametric shape modeling using deformable superellipses for prostate segmentation,' IEEE Trans. Med. Imaging, vol. 23, pp.340-349, Mar. 2004 https://doi.org/10.1109/TMI.2004.824237
  4. D. Shen, Y. Zhan, and C. Davatzikos, 'Segmentation of prostate boundaries from ultrasound images using statistical shape model,' IEEE Trans. Med. Imaging, vol. 22, pp. 539-551, Apr. 2003 https://doi.org/10.1109/TMI.2003.809057
  5. B. Nacim, V. Maximilien, P. David, M. Salah, and R. Jean, 'Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter,' Comput. Med. Imaging Graphic., vol. 29, pp. 43-51, 2005 https://doi.org/10.1016/j.compmedimag.2004.07.007
  6. S. D. Pathak, P. D. Grimm, V. Chalana, and Y. Kim, 'Pubic arch detection in transrectal ultrasound guided prostate cancer therapy,' IEEE Trans. Med. Imaging, vol. 17, pp. 762-771, Oct. 1998 https://doi.org/10.1109/42.736032
  7. B. Chiu, G. H. Freeman, M. M. A. Salama, and A. Fenster, 'Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour,' Phys. Med. Biol., vol. 49, pp. 4943-4960, Oct. 2004 https://doi.org/10.1088/0031-9155/49/21/007
  8. F. Shao, K. V. Ling, W. S. Ng, and R. Y. Wu, 'Prostate boundary detection from ultrasonographic images: review article,' Jour. Ultrasound Med., vol. 22, pp. 605-623, 2003 https://doi.org/10.7863/jum.2003.22.6.605
  9. S. D. Pathak, V. Chalana, D. R. Haynor, and Y. Kim, 'Edge-guided boundary delineation in prostate ultrasound images,' IEEE Trans. Med. Imaging, vol. 19, pp. 1211-1219, Dec. 2000 https://doi.org/10.1109/42.897813
  10. R. Sahba, H. R. Tizhoosh, and M. M. A. Salama, 'A coarse-to-fine approach to prostate boundary segmentation in ultrasound images,' BioM. Eng. Online, vol. 4, pp. 58, Oct. 2005 https://doi.org/10.1186/1475-925X-4-58
  11. M. Kass, A. Witkin, and D. Terzopoulos, 'Snakes: Active contour models.' Int. Jour. Computer Vision. vol. 1, pp. 321-331, 1987 https://doi.org/10.1007/BF00133570
  12. Z. Fangwei and C. J. S. deSilva, 'Contour extraction in prostate ultrasound images using the wavelet transform and snakes,' Proc. Int. Conf. Engin. in Medicine and Biol. Society, vol. 3, Istanbul, Turkey, Oct. 2001, pp. 2641-2544 https://doi.org/10.1109/IEMBS.2001.1017325
  13. S. Mallat and S. Zhong, 'Characterization of signals from multiscale edges,' IEEE Trans. Pattern Anal. Machine Intell., vol. 14, pp. 710-731, July 1992 https://doi.org/10.1109/34.142909
  14. I. El-Naqa, Y. Yongyi, M. N. Wernick, N. p. Galatsanos, and R. M. Nishikawa, 'A support vector machine approach for detection of microcalcifications,' IEEE Trans. Med. Imaging, vol. 21, pp. 1552-1563, Dec. 2002 https://doi.org/10.1109/TMI.2002.806569
  15. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, 'An introduction to kernel-based learning algorithms,' IEEE Trans. Neural Networks, vol. 12, pp. 181-201, Mar. 2001 https://doi.org/10.1109/72.914517
  16. Y. D. Chun, S. Y. Seo, and N. C. Kim, 'Image retrieval using BDIP and BVLC moments,' IEEE Trans. Circuits Syst. Video Technol., vol. 13, pp. 951-957, Sep. 2003 https://doi.org/10.1109/TCSVT.2003.816507
  17. E. Steen and B. Olstad, 'Volume rendering of 3D medical ultrasound data using direct feature mapping,' IEEE Trans. Med. Imaging, vol. 13, pp. 517-525, Sep. 1994 https://doi.org/10.1109/42.310883