Skip to main content
SpringerLink
Log in

New approach to the automatic segmentation of coronary artery in X-ray angiograms

  • Published:
Science in China Series F: Information Sciences Aims and scope Submit manuscript

Abstract

For the segmentation of X-ray angiograms (XRA), the essential feature and the prior knowledge of angiographic image were analyzed, and a multi-feature based fuzzy recognition (MFFR) algorithm was proposed to infer the local vessel structure in this paper. Guided by the prior knowledge of artery vessel, a probability tracking operator (PTO) can rapidly track along the artery tree, and walk across the weak region or gaps because of disturbance or preprocessing to angiographic image. Another, the accurate measurement of the vascular axis-lines and diameters can be synchronously implemented in the tracking process. To correctly evaluate the proposed method, a simulated image of CAT and some clinical XRA images were used in the experimentations. The algorithms performed better than the conventional one: given one start-point, on average 92.7% of the visible segments or branches was automatically delineated; the correctness ratio of vessel structure inference reached to 90.0% on the average.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kirbas C, Quek K H F. Vessel extraction techniques and algorithms: A survey. In: Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering (BIBE), 2003. 238–245

  2. Sun Y. Member IEEE. Automated identification of vessel contours in coronary arteriograms by an adaptive tracing algorithm. IEEE Trans Med Imaging, 1989, 8(1): 78–88

    Article  Google Scholar 

  3. Can A, Shen H, Turner N J, et al. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE T Inf Technol Biomed, 1999, 3(2): 125–138

    Article  Google Scholar 

  4. Lalonde M, Gagnon L, Boucher M C. Non-recursive paired tracing for vessel extraction from retinal images. In: Proceedings of the Conference Vision Interface 2000. Ottawa, 2000. 61–68

  5. Park S, Lee J, Koo J, et al. Adaptive tracing algorithm based on direction field using ML estimation in angiogram. In: IEEE Conference on Speech and Image Technologies for Computing and Telecommunications. Brisbane, 1997. 671–675

  6. Aylward S R, Bullit E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging, 2002, 21(2): 61–75

    Article  Google Scholar 

  7. Schrijver M, Slump C H. Automatic segmentation of the coronary artery tree in angiographic projections. In: Proceedings of ProRISC 2002. Veldhoven, 2002. 449–464

  8. Chutatape O, Zheng L, Krishnan S M. Retinal blood vessel detection and tracing by matched Gaussian and Kalman Filters. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Hong Kong, 1998. 3144–3149

  9. Yannis A T, Stavros M P. A fuzzy vessel tracing algorithm for retinal images based on fuzzy clustering. IEEE Trans on Med Imaging, 1998, 17(2): 263–273

    Article  Google Scholar 

  10. Grisan E, Pesce A, Giani A, et al. A new tracing system for the robust extraction of retinal vessel structure. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, 2004. 1620–1623

  11. Frangi A F, Niessen W J, Vincken K L, et al. Multiscale vessel enhancement filtering. In: Wells W M, Colchester A, Delp SL, eds. Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer Verlag, 1998. 130–137

    Google Scholar 

  12. Sato Y, Nakajimma S, Atsumi H, et al. 3-D Muli-Scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal, 1998, 2(2): 143–168

    Article  Google Scholar 

  13. Wink O, Wiro J N, Max A V. Multiscale Vessel Tracking. IEEE Trans Med Imaging, 2004, 23(1): 130–133

    Article  Google Scholar 

  14. Wilkinson M H F, Wijbenga T, de Vries G, et al. Blood vessel segmentation using moving-window robust automatic threshold selection. In: Proceedings of 2003 International Conference on Image Processing (ICIP), 2003. 14–17

  15. Moreno P, Bernardino A, Santos-Victor J. Model based selection and classification of local features for recognition using Gabor filters. In: Proceedings of International Conference on Image Analysis and Recognition (ICIAR), 2006. 181–192

  16. Moreno P, Bernardino A, Santos-Victor J. Gabor parameter selection for local feature detection. In: Proceedings of the 2nd Iberian Conference on Pattern Recognition and Image Analysis. Estoril: Springer Verlag, 2005. 7–9

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China

    Zhou ShouJun & Chen WuFan

  2. 458 Hospital of PLA, Guangzhou, 510000, China

    Zhou ShouJun & Yang Jun

  3. Department of Opto-electronic Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Zhou ShouJun & Wang YongTian

Authors
  1. Zhou ShouJun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen WuFan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wang YongTian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen WuFan.

Additional information

Supported by the National Basic Research Program of China (Grant No. 2003CB716101), the National Natural Science Foundation of China (Grant No. 60772120) and the Key National Science Foundation of China (Grant No. 30730026)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, S., Yang, J., Chen, W. et al. New approach to the automatic segmentation of coronary artery in X-ray angiograms. Sci. China Ser. F-Inf. Sci. 51, 25–39 (2008). https://doi.org/10.1007/s11432-008-0005-5

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-008-0005-5

Keywords

Search

Navigation