The Significance of the Vessel Registration for a Reliable Computation of Arteriovenous Ratio

  • S. G. Vázquez
  • N. Barreira
  • Manuel G. Penedo
  • M. Rodríguez-Blanco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


The arteriovenous ratio (AVR), this is, the relation between artery and vein widths, is a popular dimensionless measure to quantify changes in retinal microvasculature. However, its use in daily clinical practice has not been implanted due to the lack of reproducibility caused mainly by the laborious manual calculation and the dependence on the vessels selected for the estimation. This paper presents a vessel registration methodology in an AVR computation framework. The expert computes the AVR from a reference image in a semiautomatic manner and, after that, the AVR can be computed automatically from successive images of the same patient using the stored information from the reference image. The system has been evaluated in a large data set of 158 pairs of images and good correlation results between medical experts and system have been achieved.


arteriovenous ratio retinal imaging AVR monitoring image registration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sun, C., Wang, J.J., Mackey, D.A., Wong, T.Y.: Retinal vascular caliber: systemic, environmental, and genetic associations. Survey of Ophthalmology 54(1), 74–95 (2009)CrossRefGoogle Scholar
  2. 2.
    Stanton, A.V., Wasan, B., Cerutti, A., Ford, S., Marsh, R., Sever, P.P., Thom, S.A., Hughes, A.D.: Vascular network changes in the retina with age and hypertension. J. Hypertens. 13, 1724–1728 (1995)CrossRefGoogle Scholar
  3. 3.
    Pose-Reino, A., Gomez-Ulla, F., Hayik, B., Rodriguez-Fernndez, M., Carreira-Nouche, M.J., Mosquera-Gonzez, A., Gonzez-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
  4. 4.
    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
  5. 5.
    Knudtson, M.D., Lee, K.E., Hubbard, L.D., Wong, T.Y., Klei, R., Klein, B.E.K.: Revised formulas for summarizing retinal vessel diameters. Current Eye Research 27(3), 143–149 (2003)CrossRefGoogle Scholar
  6. 6.
    Patton, N., Aslam, T., Macgillivray, T., Dhillon, B., Constable, I.: Asymmetry of retinal arteriolar branch widths at junctions affects ability of formulae to predict trunk arteriolar widths. Invest. Ophthalmol. Vis. Sci. 47(4), 1329–1333 (2006)CrossRefGoogle Scholar
  7. 7.
    Ruggeri, A., Grisan, E., De Luca, M.: An automatic system for the estimation of generalized arteriolar narrowing in retinal images. In: IEEE EMBS Conference, Lyon, France, pp. 23–26 (2007)Google Scholar
  8. 8.
    Tramontan, L., Ruggeri, A.: Computer estimation of the avr parameter in diabetic retinopathy. In: IFMBE Proceedings, vol. 25(11), pp. 141–144 (2009)Google Scholar
  9. 9.
    Niemeijer, M., Xu, X., Dumitrescu, A.V., Gupta, P., van Ginneken, B., Folk, J.C., Abramoff, M.D.: Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011)CrossRefGoogle Scholar
  10. 10.
    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: DICTA 2010, pp. 599–603 (2010)Google Scholar
  11. 11.
    Vázquez, S.G., Barreira, N., Penedo, M.G., Rodriguez-Blanco, M., Gómez-Ulla, F., González, A., Coll de Tuero, G.: Automatic Arteriovenous Ratio Computation: Emulating the Experts. In: Camarinha-Matos, L.M., Shahamatnia, E., Nunes, G. (eds.) DoCEIS 2012. IFIP AICT, vol. 372, pp. 563–570. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Barreira, N., Ortega, M., Rouco, J., Penedo, M.G., Pose-Reino, A., Marino, C.: Semi-automatic procedure for the computation of the arteriovenous ratio in retinal images. International Journal for Computational Vision and Biomechanics 3(2), 135–147 (2010)Google Scholar
  13. 13.
    López, A.M., Lloret, D., Serrat, J., Villanueva, J.J.: Multilocal Creaseness Based on the Level-Set Extrinsic Curvature. Computer Vision and Image Understanding 77(2), 111–144 (2000)CrossRefGoogle Scholar
  14. 14.
    Van den Elsen, P., Antoine Maintz, J., Pol, E.-J.D., Viergever, M.: Automatic registration of CT and MR brain images using correlation of geometerical features. IEEE Transactions on Medical Imaging 14(2), 384–396 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • S. G. Vázquez
    • 1
  • N. Barreira
    • 1
  • Manuel G. Penedo
    • 1
  • M. Rodríguez-Blanco
    • 2
  1. 1.Varpa Group, Department of Computer ScienceUniversity of A CoruñaSpain
  2. 2.Service of OphthalmologyComplejo Hospitalario UniversitarioSantiago de CompostelaSpain

Personalised recommendations