A Novel Framework for Hyperemia Grading Based on Artificial Neural Networks
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.
- 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
- 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
- 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
- 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). http://www.sciencedirect.com/science/article/pii/S0039625700002034
- 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