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Tensor Voting Extraction of Vessel Centerlines from Cerebral Angiograms

  • Yu Ding
  • Mircea Nicolescu
  • Dan Farmer
  • Yao Wang
  • George Bebis
  • Fabien Scalzo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)

Abstract

The extraction of vessel centerlines from cerebral angiograms is a prerequisite for 2D-3D reconstruction and computational fluid dynamic (CFD) simulations. Many researchers have studied vessel segmentation and centerline extraction on retinal images while less attention and efforts have been devoted to cerebral angiography images. Since cerebral angiograms consist of vessels that are much noisier because of the possible patient movement, it is often a more challenging task compared to working on retinal images. In this study, we propose a multi-scale tensor voting framework to extract the vessel centerlines from cerebral angiograms. The developed framework is evaluated on a dataset of routinely acquired angiograms and reach an accuracy of 91.75\(\%\pm 5.07\%\) during our experiments.

Keywords

Digital Subtraction Angiography Noise Removal Main Vessel Vessel Segmentation Ground Truth Image 
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.

Notes

Acknowledgments

Prof. Scalzo was partially supported by a AHA grant 16BGIA27760152, a Spitzer grant, and received hardware donations from Gigabyte, Nvidia, and Intel.

References

  1. 1.
    Scalzo, F., Liebeskind, D.S.: Perfusion angiography in acute ischemic stroke. Comput. Math. Meth. Med. 2016(2), 1–14 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Scalzo, F., Hao, Q., Walczak, A.M., Hu, X., Hoi, Y., Hoffmann, K.R., Liebeskind, D.S.: Computational hemodynamics in intracranial vessels reconstructed from biplane angiograms. In: Bebis, G., et al. (eds.) ISVC 2010. LNCS, vol. 6455, pp. 359–367. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17277-9_37 CrossRefGoogle Scholar
  3. 3.
    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). doi: 10.1007/BFb0056195 Google Scholar
  4. 4.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)CrossRefGoogle Scholar
  5. 5.
    Hooshyar, S., Khayati, R.: Retina vessel detection using fuzzy ant colony algorithm. In: CRV, pp. 239–244 (2010)Google Scholar
  6. 6.
    Sanjani, S.S., Boin, J.B., Bergen, K.: Blood vessel segmentation in retinal fundus images (2013)Google Scholar
  7. 7.
    Sreejini, K., Govindan, V.: Improved multiscale matched filter for retina vessel segmentation using PSO algorithm. Egypt Inform. J. 16, 253–260 (2015)CrossRefGoogle Scholar
  8. 8.
    Egger, J., Mostarkic, Z., Großkopf, S., Freisleben, B.: A fast vessel centerline extraction algorithm for catheter simulation. In: CBMS, pp. 177–182 (2007)Google Scholar
  9. 9.
    Sofka, M., Stewart, C.V.: Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imaging 25, 1531–1546 (2006)CrossRefGoogle Scholar
  10. 10.
    Xu, Y., Zhang, H., Li, H., Hu, G.: An improved algorithm for vessel centerline tracking in coronary angiograms. Comput. Methods Programs Biomed. 88, 131–143 (2007)CrossRefGoogle Scholar
  11. 11.
    Puentes, J., Roux, C., Garreau, M., Coatrieux, J.L.: Dynamic feature extraction of coronary artery motion using dsa image sequences. IEEE Trans. Med. Imaging 17, 857–871 (1998)CrossRefGoogle Scholar
  12. 12.
    Tang, C.K., Medioni, G.: Curvature-augmented tensor voting for shape inference from noisy 3d data. IEEE Trans. Pattern Anal. Mach. Intell. 24, 858–864 (2002)CrossRefGoogle Scholar
  13. 13.
    Jia, J., Tang, C.K.: Inference of segmented color and texture description by tensor voting. IEEE Trans. Pattern Anal. Mach. Intell. 26, 771–786 (2004)CrossRefGoogle Scholar
  14. 14.
    Jia, J., Tang, C.K.: Image repairing: robust image synthesis by adaptive ND tensor voting. In: CVPR, vol. 1, pp. I–643 (2003)Google Scholar
  15. 15.
    Loss, L.A., Bebis, G., Parvin, B.: Iterative tensor voting for perceptual grouping of ill-defined curvilinear structures. IEEE Trans. Med. Imaging 30, 1503–1513 (2011)CrossRefGoogle Scholar
  16. 16.
    Risser, L., Plouraboué, F., Descombes, X.: Gap filling of 3-d microvascular networks by tensor voting. IEEE Trans. Med. Imaging 27, 674–687 (2008)CrossRefGoogle Scholar
  17. 17.
    Leng, Z., Korenberg, J.R., Roysam, B., Tasdizen, T.: A rapid 2-d centerline extraction method based on tensor voting. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1000–1003 (2011)Google Scholar
  18. 18.
    Medioni, G., Lee, M.S., Tang, C.K.: A computational framework for segmentation and grouping. Elsevier (2000)Google Scholar
  19. 19.
    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975)Google Scholar
  20. 20.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Upper Saddle River (1989)MATHGoogle Scholar
  21. 21.
    Medioni, G., Tang, C.K., Lee, M.S.: Tensor voting: theory and applications. In: Proceedings of RFIA, Paris, France, vol. 3 (2000)Google Scholar
  22. 22.
    Wu, T.P., Yeung, S.K., Jia, J., Tang, C.K., Medioni, G.: A closed-form solution to tensor voting: theory and applications. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1482–1495 (2012)CrossRefGoogle Scholar
  23. 23.
    Loss, L., Bebis, G., Nicolescu, M., Skurikhin, A.: An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds. Comput. Vis. Image Underst. 113, 126–149 (2009)CrossRefGoogle Scholar
  24. 24.
    Deutsch, S., Medioni, G.: Intersecting manifolds: detection, segmentation, and labeling. In: AAAI, pp. 3445–3452 (2015)Google Scholar
  25. 25.
    Christodoulidis, A., Hurtut, T., Tahar, H.B., Cheriet, F.: A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images. Comput. Med. Imaging Graph. 52, 28–43 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yu Ding
    • 1
  • Mircea Nicolescu
    • 2
  • Dan Farmer
    • 2
  • Yao Wang
    • 1
  • George Bebis
    • 2
  • Fabien Scalzo
    • 1
  1. 1.Department of NeurologyUniversity of California, Los Angeles (UCLA)Los AngelesUSA
  2. 2.Department of Computer ScienceUniversity of Nevada, Reno (UNR)RenoUSA

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