Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Carotid Artery Image Segmentation using Snake Based Model

스네이크모델을 기반으로 한 경동맥 이미지분할

  • Received : 2013.02.08
  • Accepted : 2013.02.28
  • Published : 2013.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Disease diagnostics based on medical imaging is getting popularity day by day. Presence of the atherosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present an automatic image segmentation technique for carotid artery ultrasound images based on active contour approach. In our experimental study, we assume that ultrasound images are properly aligned before applying automatic image segmentation. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Our proposed approach successfully segments the carotid artery images in an automated way to help radiologists to detect plaque easily. Obtained results show the effectiveness of the proposed approach.

최근 의료영상을 이용한 질병 진단법에 대한 관심이 증가하고 있는 추세이다. 관절경화증은 경동맥의 동맥을 좁게 하여 뇌로 들어가는 혈류의 일부 또는 전체를 차단하는 원인이 된다. 뇌로 흘러가는 혈류가 차단되는 경우 심각한 뇌졸중을 야기하기도 한다. 만일 초기에 경동맥 플라크를 발견하고 이를 치료하면 심각한 뇌졸중을 예방할 수 있다. 본 논문에서는 경동맥의 동맥 초음파 영상에서 경동맥 플라크를 쉽게 발견하기 위한 능동적 윤곽선 추출기법에 기반을 둔 자동 분할기법을 제안한다. 실험에서 사용되는 초음파 영상은 자동 분할기법을 적용하기 전에 적절히 정렬되어있다고 가정한다. 경동맥의 동맥 초음파 영상에 대하여 스네이크 모델을 이용하여 자동분할 방법과 수동분할 방법을 질적 비교한 결과 제안된 방법이 성공적으로 적용되었음을 보여준다. 실험결과 제안된 방법은 방사선사들이 플라크를 쉽게 찾는데 도움을 줄 수 있는 자동화 방법이 될 것으로 예상된다.

Keywords

References

  1. M. Kamel, A. Campilho, A. R. Abdel-Dayem and M. R. El-Sakka, "Fuzzy C-Means Clustering for Segmenting Carotid Artery Ultrasound Images," Lecture Notes in Computer Science, Vol. 4633, pp. 935-948, Aug, 2007.
  2. M. Kamel, A. Campilho, A. R. Abdel-Dayem, and M. R. El-Sakka, "Carotid Artery Ultrasound Image Segmentation Using Fuzzy Region Growing," Lecture Notes in Computer Science, Vol. 3656, pp. 869-878, Sep, 2005.
  3. M. Ceccarelli, N. De Luca, and A. Morganella, "An Active Contour Approach to Automatic Detection of the Intima-Media Thickness," IEEE International Conference on Acoustics Speech and Signal Processing, Vol. 2, pp. 709-712, May, 2006.
  4. T. Gustavsson, L. Quan, I. Wendelhag, and J. Wikstrand, "A dynamic programming procedure for automated ultrasonic measurement of the carotid artery," Computers in Cardiology 1994, pp. 297-300, Sep, 1994.
  5. R. Rui, C. Aurelio, S. Jorge, A. Elsa and S. Rosa, "Segmentation of the carotid intima-media region in B-mode ultrasound images," Image and Vision Computing, Vol. 28, No. 4, pp. 614-625, April, 2010. https://doi.org/10.1016/j.imavis.2009.09.017
  6. S. G. Moursi and M. R. E. Sakka, "Semi-automatic snake-based segmentation of carotid artery ultrasound images," Communications of the Arab Computer Society (ACS), Vol.2, No. 2, pp. 1-32, Oct, 2009.
  7. D. L. Pham, C. Xu and J. L. Prince, "Current Methods in Medical Image Segmentation," In Annual Review of Biomedical Engineering, Vol. 2, pp. 315-337, Aug, 2000. https://doi.org/10.1146/annurev.bioeng.2.1.315
  8. N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man., and Cybernetics, Vol. 9, No. 1, pp. 62-66, Jan, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  9. R. C. Gonzalez and R. E. Woods, "Digital Image Processing, Third Edition," Pearson Prentice Hall. Aug, 2007.
  10. M. Kass, A. Witkin and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, Vol. 1, No. 4, pp. 321-331, Jan, 1988. https://doi.org/10.1007/BF00133570
  11. P. Brigger, J. Hoeg and M. Unser, "B-spline snakes: a flexible tool for parametric contour detection. IEEE Transactions on Image Processing, Vol. 9, No. 9, pp. 1484-1496, Sep, 2000. https://doi.org/10.1109/83.862624
  12. J. Liang, T. McInerny and D. Terzopoulos, "United Snakes [image analysis software]," The proceedings of the seventh IEEE International Conference on Computer Vision, Vol. 2, pp. 933-940, Sep, 1999.
  13. L. Weruaga, R. Verdu and J. Morales, "Frequency domain formulation of active parametric deformable models," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 12, pp. 1568-1578, Dec, 2004. https://doi.org/10.1109/TPAMI.2004.124
  14. M. Unser, "Splines: A perfect fit for medical imaging," SPIE Symposium on Medical Image, Vol. 4684, pp. 225-236, Fab, 2002.