A Novel Framework for Hyperemia Grading Based on Artificial Neural Networks

  • Luisa SánchezEmail author
  • Noelia Barreira
  • Hugo Pena-Verdeal
  • Eva Yebra-Pimentel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


A common symptom of several pathologies is hyperemia, that occurs when a certain tissue has an abnormal hue of red. An increase of blood flow causes the engorgement of blood vessels, which produces the coloration. Hyperemia is an important parameter that specialists take into account when diagnosing diseases such as dry eye syndrome or problems derived from contact lenses wearing. In this work, we propose an automatic methodology to measure the hyperemia level of the bulbar conjunctiva. This methodology emphasizes the transformation from the extracted features to grading scales, using artificial neural networks for the process.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bailey, I., Bullimore, M., Raasch, T., Taylor, H.: Clinical grading and the effects of scaling. Investigative ophthalmology & visual science 32(2), 422–432 (1991)Google Scholar
  2. 2.
    Baum, E.B.: On the capabilities of multilayer perceptrons. Journal of complexity 4(3), 193–215 (1988)zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 679–698 (1986)CrossRefGoogle Scholar
  4. 4.
    Efron, N., Morgan, P.B., Katsara, S.S.: Validation of grading scales for contact lens complications. Ophthalmic and Physiological Optics 21(1), 17–29 (2001)Google Scholar
  5. 5.
    Gardner, M., Dorling, S.: Artificial neural networks (the multilayer perceptron)a review of applications in the atmospheric sciences. Atmospheric environment 32(14), 2627–2636 (1998)CrossRefGoogle Scholar
  6. 6.
    Papas, E.B.: Key factors in the subjective and objective assessment of conjunctival erythema. Investigative ophthalmology & visual science 41(3), 687–691 (2000)Google Scholar
  7. 7.
    Park, I.K., Chun, Y.S., Kim, K.G., Yang, H.K., Hwang, J.M.: New clinical grading scales and objective measurement for conjunctival injection. Investigative ophthalmology & visual science 54(8), 5249–5257 (2013)CrossRefGoogle Scholar
  8. 8.
    Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural computation 3(2), 246–257 (1991)CrossRefGoogle Scholar
  9. 9.
    Park, J., Sandberg, I.W.: Approximation and radial-basis-function networks. Neural computation 5(2), 305–316 (1993)CrossRefGoogle Scholar
  10. 10.
    Rolando, M., Zierhut, M.: The ocular surface and tear film and their dysfunction in dry eye disease. Survey of Ophthalmology 45, Supplement 2(0), S203–S210 (2001).
  11. 11.
    Schulze, M.M., Jones, D.A., Simpson, T.L.: The development of validated bulbar redness grading scales. Optometry & Vision Science 84(10), 976–983 (2007)CrossRefGoogle Scholar
  12. 12.
    Sun, Y., Duthaler, S., Nelson, B.J.: Autofocusing algorithm selection in computer microscopy. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 70–76. IEEE (2005)Google Scholar
  13. 13.
    Wolffsohn, J.S., Purslow, C.: Clinical monitoring of ocular physiology using digital image analysis. Contact Lens and Anterior Eye 26(1), 27–35 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Luisa Sánchez
    • 1
    Email author
  • Noelia Barreira
    • 1
  • Hugo Pena-Verdeal
    • 2
  • Eva Yebra-Pimentel
    • 2
  1. 1.VARPA Group, Departament of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Optometry Group, Department of Applied PhysicsUniversity of Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations