Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2010, Article ID 580518, 20 pages
Research Article

2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., H/Y Building-Zografou Campus, 15773 Athens, Greece

Received 1 October 2009; Revised 8 February 2010; Accepted 12 April 2010

Academic Editor: Guo W. Wei

Copyright © 2010 Sotirios Raptis and Dimitris Koutsouris. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Sofka and C. V. Stewart, “Retinal vessel extraction using multi-scale matched filters confidence and edge detection,” IEEE Transactions on Medical Imaging, vol. 25, no. 12, pp. 1531–1546, 2006. View at Google Scholar
  2. T. McInerney and D. Terzopoulos, “T-snakes: topology adaptive snakes,” Medical Image Analysis, vol. 4, no. 2, pp. 73–91, 2000. View at Google Scholar
  3. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269, 1989. View at Publisher · View at Google Scholar · View at PubMed
  4. J. H. Hipwell, G. P. Penney, T. C. Cox, J. V. Byrne, and D. J. Hawkes, “3d intensity based registration of dsa and mra—a comparison of similarity measure,” in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '02), vol. 2489 of Lecture Notes in Computer Science, pp. 501–508, Tokyo, Japan, 2002.
  5. A. C. S. Chung, J. A. Noble, and P. Summers, “Vascular segmentation of phase contrast magnetic resonance angiograms based on statistical mixture modeling and local phase coherence,” IEEE Transactions on Medical Imaging, vol. 23, no. 12, pp. 1490–1507, 2004. View at Publisher · View at Google Scholar · View at PubMed
  6. A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000. View at Publisher · View at Google Scholar · View at PubMed
  7. M. E. Martinez-Perez, A. D. Hughes, A. V. Stanton et al., “Retinal vascular tree morphology: a semi-automatic quantification,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 8, pp. 912–917, 2002. View at Publisher · View at Google Scholar · View at PubMed
  8. C. H. Chen, L. F. Pau, and P. S. P. Wang, Eds., The Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, Singapore, 2nd edition, 1998.
  9. Y. M. Kadah, A. A. Farag, J. M. Zurada, A. M. Badawi, and A.-B. M. Youssef, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 466–478, 1996. View at Google Scholar
  10. STARE, The STARE database,
  11. W. Niessen, C. Bouma, K. Vincken, and M. Viergever, “Error metrics for quantitative evaluation of medical image segmentation,” in Performance Characterization in Computer Vision, R. Klette, H. Stiehl, M. Viergever, and K. Vincken, Eds., pp. 275–284, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000. View at Google Scholar
  12. J. Hu, R. Kahsi, D. Lopresti, G. Nagy, and G. Wilfong, “Why table ground-truthing is hard,” in Proceedings of the 6th International Conference on Document Analysis and Recogniton, pp. 129–133, Seattle, Wash, USA, 2001.
  13. DRIVE, The University of Utrecht DRIVE database,