Model-Guided Extraction of Coronary Vessel Structures in 2D X-Ray Angiograms

  • Shih-Yu Sun
  • Peng Wang
  • Shanhui Sun
  • Terrence Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


Analysis of vessel structures in 2D X-ray angiograms is important for pre-operative evaluation and image-guided intervention. However, automated vessel segmentation in angiograms, especially extraction of the topology such as bifurcations and vessel crossings, remains challenging mainly due to the projective nature of angiography and background clutter. In this paper, a novel framework for model-guided coronary vessel extraction in 2D angiograms is presented. In this framework, a graph is constructed using a sparse set of pixels in the angiogram. With a single user-supplied click as the starting point, the vessel tree structure in the angiogram is automatically extracted from the graph. Ambiguities in this tree structure caused by 3D-to-2D projection are then resolved using topological information from the 3D vessel model of the same patient. By incorporating this prior shape information, the proposed method is effective in extraction of vessel topology, and is robust to background clutter and uneven illumination. Through quantitative evaluation on 20 angiograms, it is shown that this model-guided approach significantly improves detection of vessel structures and bifurcations.


Compute Tomography Angiography Vessel Segment Background Clutter Vessel Structure Vessel Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Estrada, R., Tomasi, C., Cabrera, M.T., Wallace, D.K., Freedman, S.F., Farsiu, S.: Exploratory Dijkstra Forest Based Automatic Vessel Segmentation: Applications in Video Indirect Ophthalmoscopy (VIO). Biomedical Optics Express 3(2), 327–339 (2012)CrossRefGoogle Scholar
  2. 2.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale Vessel Enhancement Filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Kirbas, C., Quek, F.: A Review of Vessel Extraction Techniques and Algorithms. ACM Computing Surveys (CSUR) 36(2), 81–121 (2004)CrossRefGoogle Scholar
  4. 4.
    Pechaud, M., Keriven, R., Peyre, G.: Extraction of Tubular Structures Over an Orientation Domain. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 336–342. IEEE Press (2009)Google Scholar
  5. 5.
    Poon, K., Hamarneh, G., Abugharbieh, R.: Live-Vessel: Extending Livewire for Simultaneous Extraction of Optimal Medial and Boundary Paths in Vascular Images. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 444–451. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Rivest-Henault, D., Sundar, H., Cheriet, M.: Nonrigid 2D/3D Registration of Coronary Artery Models with Live Fluoroscopy for Guidance of Cardiac Interventions. IEEE Transactions on Medical Imaging 31(8), 1557–1572 (2012)CrossRefGoogle Scholar
  7. 7.
    Sofka, M., Stewart, C.V.: Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures. IEEE Transactions on Medical Imaging 25(12), 1531–1546 (2006)CrossRefGoogle Scholar
  8. 8.
    Wink, O., Niessen, W.J., Viergever, M.A.: Multiscale Vessel Tracking. IEEE Transactions on Medical Imaging 23(1), 130–133 (2004)CrossRefGoogle Scholar
  9. 9.
    Yim, P.J., Choyke, P.L., Summers, R.M.: Gray-Scale Skeletonization of Small Vessels in Magnetic Resonance Angiography. IEEE Transactions on Medical Imaging 19(6), 568–576 (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shih-Yu Sun
    • 1
  • Peng Wang
    • 1
  • Shanhui Sun
    • 1
  • Terrence Chen
    • 1
  1. 1.Siemens Corporation, Corporate TechnologyPrincetonUSA

Personalised recommendations