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Automatic Arteriovenous Ratio Computation: Emulating the Experts

  • S. G. Vázquez
  • N. Barreira
  • M. G. Penedo
  • M. Rodriguez-Blanco
  • F. Gómez-Ulla
  • A. González
  • G. Coll de Tuero
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 372)

Abstract

The arteriovenous ratio is an objective way to assess the arteriolar narrowing related to several diseases such as hypertension. It is computed as the ratio between the artery and vein mean widths. However, its calculus is not straightforward since the experts do not use all the retinal vessels. This paper presents an automatic, precise and reproducible methodology for the AVR computation. We analyze the way the experts select the vessels in order to build a system which emulates them. The system was evaluated by two ophthalmologists in a data set of 86 images. The correlation results among the system and the experts are an indication of the reproducibility of the results.

Keywords

arteriovenous ratio computation retinal imaging vessel segmentation vessel classification 

References

  1. 1.
    Hubbard, L.D., Brothers, R.J., King, W.N., Clegg, L.X., Klein, R., Cooper, L.S., Sharrett, A.R., Davis, M.D., Cai, J.: Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities studies. Ophthalmology 106, 2269–2280 (1999)CrossRefGoogle Scholar
  2. 2.
    Wong, T.Y., Klein, R., Klein, B.E.K., Tielsch, J.M., Hubbard, L., Javier Nieto, F.: Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Survey of Ophthalmology 46(1), 59–80 (2001)CrossRefGoogle Scholar
  3. 3.
    Li, H., Hsu, W., Lee, M.L., Wong, T.Y.: Automatic grading of retinal vessel width. Heart 52(7), 1352–1355 (2009)Google Scholar
  4. 4.
    Pose-Reino, A., Gómez-Ulla, F., Hayik, B., Rodríguez-Fernández, M., Carreira-Nouche, M.J., Mosquera-González, A., González-Penedo, M., Gude, F.: Computerized measurement of retinal blood vessel calibre: description, validation and use to determine the influence of ageing and hypertension. Journal of Hypertension 23(4), 843–850 (2005)CrossRefGoogle Scholar
  5. 5.
    Tramontan, L., Grisan, E., Ruggeri, A.: An improved system for the automatic estimation of the arteriolar-to-venular diameter ratio (avr) in retinal images. In: 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, pp. 3550–3553 (2008)Google Scholar
  6. 6.
    Ortega, M., Barreira, N., Novo, J., Penedo, M.G., Pose-Reino, A., Gómez-Ulla, F.: Sirius: A web-based system for retinal image analysis. International Journal of Medical Informatics 79, 722–732 (2010)CrossRefGoogle Scholar
  7. 7.
    Blanco, M., Penedo, M.G., Barreira, N., Penas, M., Carreira, M.J.: Localization and Extraction of the Optic Disc Using the Fuzzy Circular Hough Transform. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 712–721. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Lopez, A.M., Lloret, D., Serrat, J., Villanueva, J.J.: Multilocal creaseness based on the level-set extrinsic curvature. Computer Vision and Image Understanding 77, 111–144 (2000)CrossRefGoogle Scholar
  9. 9.
    Caderno, I.G., Penedo, M.G., Barreira, N., Mariño, C., González, F.: Precise Detection and Measurement of the Retina Vascular Tree. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications (IAPC Nauka/Interperiodica) 15(2), 523–526 (2005)Google Scholar
  10. 10.
    Vázquez, S.G., Barreira, N., Penedo, M.G., Ortega, M., Pose-Reino, A.: Improvements in retinal vessel clustering techniques:towards the automatic computation of the arteriovenous ratio. Computing, Archives for Scientific Computing 90(3), 197–217 (2010)zbMATHCrossRefGoogle Scholar
  11. 11.
    Vázquez, S.G., Cancela, B., Barreira, N., Penedo, M.G., Saez, M.: On the automatic computation of the arterio-venous ratio in retinal images: Using minimal paths for the artery/vein classifications. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010), pp. 599–603 (2010)Google Scholar
  12. 12.
    Pose-Reino, A., Pena Seijo, M., González Penedo, M., Ortega Hortas, M., Rodríguez, M.: Estimation of the retinal microvascular calibre in hypertensive patients with the snakes semiautomatic model. Med Clin (Barc) 135(4), 145–150 (2010)CrossRefGoogle Scholar
  13. 13.
    Ortega, M., Rouco, J., Novo, J., Penedo, M.G.: Vascular Landmark Detection in Retinal Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 211–217. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Stanton, A., Mullaney, P., Fainsia, M., O’Brien, E., O’Malley, K.: A method of quantifying retinal microvascular alterations associated with blood pressure and age. Journal of Hypertension 13(1), 41–48 (1995)CrossRefGoogle Scholar
  15. 15.
    Michael, D., Knudtson, K.E., Lee, L.D.: Revised formulas for summarizing retinal vessel diameters. Current Eye Research 27(3), 143–149 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • S. G. Vázquez
    • 1
  • N. Barreira
    • 1
  • M. G. Penedo
    • 1
  • M. Rodriguez-Blanco
    • 2
  • F. Gómez-Ulla
    • 2
  • A. González
    • 1
  • G. Coll de Tuero
    • 3
  1. 1.Varpa Group, Department of Computer ScienceUniversity of A CoruñaSpain
  2. 2.Service of OphthalmologyComplejo Hospitalario Univ.Santiago de CompostelaSpain
  3. 3.Research Unit.Health Care InstituteGironaSpain

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