Measurement of Parameters of the Optic Disk in Ophthalmoscopic Color Images of Human Retina

  • Edgardo M. Felipe Riverón
  • Mijail del Toro Céspedes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


The objective of this paper is to measure some important parameters of the optic disk (or optic papilla) in ophthalmoscopic color images of human retinas. The approach consists of locating the optic disk automatically, segmenting its contour and the contour of the depression-like feature caused by glaucoma, called an excavation or cup. Then the corresponding areas are measured to calculate the ratio Cup/Disc and the relative displacement of the centroids of both regions. To achieve these objectives, noise is filtered, luminance is normalized, and a thresholding technique is used. The results obtained will aid the work of ophthalmologists by increasing the quality of automatic diagnosis of glaucoma, one of the main causes of blindness worldwide.


Optic Disk Active Contour Human Retina Flat Disk External Border 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Alemañy Martorell, J., Marrero Faz, E., Villar Valdés, R.: Oftalmología. Capítulo 14 (1980)Google Scholar
  2. 2.
    Lubinus Badillo, F.G., Mantilla Suárez, J.C., Valencia, A., Rueda, J.C.: Estudio de la circulación retrobulbar con imagen doppler a color en pacientes con glaucoma asimétrico (2002)Google Scholar
  3. 3.
    Gagnon, L., Lalonde, M., Beaulieu, M., Boucher, M.-C.: Procedure to detect anatomical structures in optic fundus images, Computer Research Institute of Montreal. Dept. of Ophthalmology, Maisonneuve-Rosemont Hospital (2001)Google Scholar
  4. 4.
    Li, H., Chutatape, O.: Automatic Location of Optic Disk in Retinal Image. School of Electrical and Electronic Engineering (1999)Google Scholar
  5. 5.
    Molina, R.: Introducción al Procesamiento y Análisis de Imágenes Digitales, en,
  6. 6.
    McLaughlin, R.A.: Technical Report – Randomized Hough Transform: Improved Ellipse Detection with Comparison (1995)Google Scholar
  7. 7.
    Mendels, F., Heneghan, C., Thiran, J.P.: Identification of the Optic disk boundary in retinal images using active contours (2000)Google Scholar
  8. 8.
    Otsu, N.: A threshold Selection Method from Gray-Level Histogram. IEEE Trans Systems, Man and Cybernetics SMC-9, 62–66 (1976)Google Scholar
  9. 9.
    Rodriguez, R., Alarcon, T.E.: Color Segmentation Applied to the Study of the Angiogenesis (2002)Google Scholar
  10. 10.
    Vincent, L.: Componentes de SIGAU, Capítulo 5 (1999)Google Scholar
  11. 11.
    Vincent, L.: Morphological grayscale Reconstruction in Images Analysis: Applications and Efficient Algorithms. IEEE Transactions on Images Processing 2, 176–201 (1993)CrossRefGoogle Scholar
  12. 12.
    Image Processing Learning Resources,
  13. 13.
    Felipe-Riverón, E.M.: Medidas de distancias, curso sobre Conceptos básicos sobre procesamiento de imágenes, CIC-IPN (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Edgardo M. Felipe Riverón
    • 1
  • Mijail del Toro Céspedes
    • 2
  1. 1.Center for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Havana UniversityHavanaCuba

Personalised recommendations