Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2011, Article ID 749054, 7 pages
http://dx.doi.org/10.1155/2011/749054
Research Article

Retinal Image Matching Using Hierarchical Vascular Features

1Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
2Centre for Eye Research Australia, The University of Melbourne, Melbourne, VIC 3002, Australia

Received 16 March 2011; Revised 29 July 2011; Accepted 1 August 2011

Academic Editor: Andrzej Cichocki

Copyright © 2011 Alauddin Bhuiyan et al. 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. T. Y. Wong, A. Kamineni, R. Klein et al., “Quantitative retinal venular caliber and risk of cardiovascular disease in older persons: the cardiovascular health study,” Archives of Internal Medicine, vol. 166, no. 21, pp. 2388–2394, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Y. Wong, R. Klein, B. E. K. Klein, S. M. Meuer, and L. D. Hubbard, “Retinal vessel diameters and their associations with age and blood pressure,” Investigative Ophthalmology and Visual Science, vol. 44, no. 11, pp. 4644–4650, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. I. W. Abrahams and D. S. Gregerson, “Longitudinalstudy of serum antibody responses to bovine retinal santigenin endogenous granulomatous uveitis,” British Journal of Ophthalmology, vol. 67, pp. 681–684, 1983. View at Google Scholar
  4. M. R. Stanford, E. Graham, E. Kasp, M. D. Sanders, and D. C. Dumonde, “A longitudinal study of clinicaland immunological findings in 52 patients withrelapsing retinal vasculitis,” British Journal of Ophthalmology, vol. 72, pp. 442–447, 1988. View at Google Scholar
  5. A. D. Hughes, A. V. Stanton, A. S. Jabbar, N. Chapman, M. E. Martinez-Perez, and S. A. McG Thom, “Effect of antihypertensive treatment on retinal microvascular changes in hypertension,” Journal of Hypertension, vol. 26, no. 8, pp. 1703–1707, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. N. C. B. B. Taarnhoj, I. C. Munch, B. Sander, L. Kessel, J. L. Hougaard, and K. Kyvik, “Straight versustortuous retinal arteries in relation to blood pressureand genetics,” British Journal of Ophthalmology, vol. 92, pp. 1055–1060, 2008. View at Google Scholar
  7. A. J. Harris and D. C. Yen, “Biometric authentication: assuring access to information,” Information Management and Computer Security, vol. 10, no. 1, pp. 12–19, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Marino, M. G. Penedo, M. Penas, M. J. Carreira, and F. Gonzalez, “Personal authentication usingdigital retinal images,” Pattern Analysis Application, vol. 9, pp. 21–33, 2006. View at Google Scholar
  9. M. Womack, “The eyes have it,” Sensor Review, vol. 14, pp. 15–16, 1994. View at Google Scholar · View at Scopus
  10. K. Deng, J. Tian, J. Zheng, X. Zhang, X. Dai, and M. Xu, “Retinal fundus image registration via vascular structure graph matching,” International Journal of Biomedical Imaging, vol. 2010, Article ID 906067, 13 pages, 2010. View at Publisher · View at Google Scholar
  11. X. Guo, W. Hsu, M. L. Lee, and T. Y. Wong, “A tree matching approach for the temporal registration of retinal images,” in Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI '06), pp. 632–639, 2006. View at Publisher · View at Google Scholar
  12. K. S. Lin, C. L. Tsai, M. Sofka, C. H. Tsai, S. J. Chen, and W. Y. Lin, “Vascular tree construction with anatomical realism for retinal images,” in Proceedings of the 9th IEEE International Conference on Bioinformatics and BioEngineering (BIBE '09), pp. 313–318, 2009. View at Publisher · View at Google Scholar
  13. A. Bhuiyan, B. Nath, J. Chua, and R. Kotagiri, “Blood vessel segmentation from color retinal images using unsupervised texture classification,” in Proceedings of the14th IEEE International Conference on Image Processing (ICIP '07), pp. V521–V524, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Bhuiyan, B. Nath, J. Chua, and K. Ramamohanarao, “Automatic detection of vascular bifurcationsand crossovers from color retinal fundus images,” in Proceedingsof 3rd International IEEE Conference on Signal-Image Technologies and Internet-Based System (SITIS '07), pp. 711–718, 2007.
  15. A. Bhuiyan, B. Nath, J. Chua, and R. Kotagiri, “Vessel cross-sectional diameter measurement on color retinal image,” Communications in Computer and Information Science, vol. 25, pp. 214–227, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson Prentice Hall, Upper Saddle River, NJ, USA, 3rd edition, 2008.
  17. A. Bhuiyan, Automated modeling of retinal vascularnetwork, Ph.D. thesis, The University of Melbourne, Victoria, Australia, 2009.
  18. D. E. Knuth, The Art of Computer Programming, Addison-Wesley, Boston, Mass, USA, 2nd edition, 1973.