Identification and Prevention of Glaucoma Through Digital Processing of Biomedical Imaging by the Relationship Between Volume of Nerve Fibers and Intraocular Pressure

  • Eduardo Pinos-VélezEmail author
  • Marlon Chazi
  • Christian Cajamarca
  • Vladimir Robles-Bykbaev
  • Carlos Luis Chacón
  • William Ipanaqué
  • Luis Serpa-Andrade
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1205)


Glaucoma is the second leading cause of blindness worldwide and the first in an irreversible way, many of the studies and investigations are concentrated in methods of control and treatment, however, there are few studies carried out in the areas of prevention and early detection of glaucoma, so, in this research presents the effect of increased intraocular pressure in the optic nerve, especially in damage caused in the layer of nerve fibers and their impact with the visual field, To do this, you work with background images of human eye, obtained from the Clinic “Santa Lucia”, digital image processing to characterize parameters that determine a presumptive diagnosis of glaucoma suspect, the results are presented to specialists for a medical diagnosis.


Intraocular pressure Glaucoma Retinal fiber nerve Optical coherence tomography Image processing 


  1. 1.
    Quigley, H.A., Broman, A.T.: The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90(3), 262–267 (2006)CrossRefGoogle Scholar
  2. 2.
    Harizman, N., Oliveira, C., Chiang, A., Tello, C., Marmor, M., Ritch, R., Liebmann, J.M.: The ISNT rule and differentiation of normal from glaucomatous eyes. Arch. Ophthalmol. 124(11), 1579–1583 (2006)CrossRefGoogle Scholar
  3. 3.
    Morales, S., Naranjo, V., Angulo, J., Alcañiz, M.: Automatic detection of optic disc Based on PCA and mathematical morphology. IEEE Trans. Med. Imaging 32(4), 786–796 (2013)CrossRefGoogle Scholar
  4. 4.
    González Martin-Moro, J.: Manual CTO de Medicina y Cirugía. Oftalmología. CTO Editorial (2011)Google Scholar
  5. 5.
    Pinos, E.G.: Método de detección por tonometría para el diagnóstico temprano del glaucoma a través de herramientas de simulación. Centro de Investigación, Desarrollo e Innovación en Ingenierías-CIDII, Ecuador (2013)Google Scholar
  6. 6.
    Organización Mundial de la Salud, Ceguera y discapacidad visual; datos y cifras (2017)Google Scholar
  7. 7.
    Rodrigo, B.: Detección de glaucoma mediante el análisis de imagen de fondo de ojo, Instituto Interuniversitario de Investigación en Bioingeniería Orientada al Ser Humano (I3BH). Universidad Politécnica de Valencia (2014)Google Scholar
  8. 8.
    Jose, A.M., Balakrishnan, A.A.: A novel method for glaucoma detection using optic disc and cup segmentation in digital retinal fundus images. In: Proceedings of 2015 International Conference on Circuits, Power and Computing Technologies (ICCPCT-2015), Nagercoil, pp. 1–5 (2015)Google Scholar
  9. 9.
    Rodrigo, B., Morales, S., Naranjo, V., Colomer, A., Alcañiz Raya, M.: Detección de glaucoma mediante la combinación de la relación copa/disco y la regla INST. In: Proceedings of Congreso Annual de la Socieldad Española de Ingerieria Biomedica (CASEIB), Barcelona, España (2014)Google Scholar
  10. 10.
    Prageeth, P.G., Sukesh Kumar, D.J.A.: Early detection of retinal nerve fiber layer defects using fundus image processing. IEEE Recent Adv. Intell. Comput. Syst. 2011, 930–936 (2011)Google Scholar
  11. 11.
    Marín, D.M.C.P.: Óptica Fisiológica: El sistema óptico del ojo y la visión binocular (2006)Google Scholar
  12. 12.
    Villanueva Coello, B., Manuel, J., Cotutor, M., Morales Martínez, S.: Análisis de imágenes oftalmológicas de Tomografía por Coherencia Óptica OCT (2016)Google Scholar
  13. 13.
    De GonzálezLaRosa, M., González-Hernández, M., García-Feijoo, J., SánchezMéndez, M., García-Sánchez, J.: Comparación del rango de medida de defectos entre la perimetría estándar blanco/blanco y la perimetría Pulsar. Arch. Soc. Esp. Oftalmol. 86(4), 113–117 (2010)Google Scholar
  14. 14.
    Strouthidis, N.G., Scott, A., Peter, N.M., Garway-Heath, D.F.: Optic disc and visual field progression in ocular hypertensive subjects: detection rates, specificity, and agreement. Invest. Opthalmol. Vis. Sci. 47(7), 2904 (2006)CrossRefGoogle Scholar
  15. 15.
    Youngquist, R.C., Carr, S., Davies, D.E.N.: Optical coherence-domain reflectometry: a new optical evaluation technique. Opt. Lett. 12(3), 158 (1987)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Eduardo Pinos-Vélez
    • 1
    Email author
  • Marlon Chazi
    • 2
  • Christian Cajamarca
    • 2
  • Vladimir Robles-Bykbaev
    • 2
  • Carlos Luis Chacón
    • 3
  • William Ipanaqué
    • 4
  • Luis Serpa-Andrade
    • 1
  1. 1.GIIATA, Research Group on Artificial Intelligence and Assistive Technologies and GIIB, Research Group on Biomedical EngineeringUniversidad Politécnica SalesianaCuencaEcuador
  2. 2.GIIATA, Research Group on Artificial Intelligence and Assistive TechnologiesUniversidad Politécnica SalesianaCuencaEcuador
  3. 3.Ecuadorian Society of GlaucomaClinical Santa LucíaQuitoEcuador
  4. 4.PhD of Engineering Computer Science and Control, DICOPUniversidad de PiuraPiuraPeru

Personalised recommendations