Advertisement

Image Processing Techniques for Glaucoma Detection

  • Mishra Madhusudhan
  • Nath Malay
  • S. R. Nirmala
  • Dandapat Samerendra
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)

Abstract

Glaucoma is a disease caused due to neurodegeneration of the optic nerve which leads to blindness. It can be evaluated by monitoring intra ocular pressure (IOP), visual field and the optic disc appearance (cup-to-disc ratio). Glaucoma increases the cup to disc ratio (CDR), affecting the peripheral vision loss. This paper addresses the various image processing techniques to diagnose the glaucoma based on the CDR evaluation of preprocessed fundus images. These algorithms are tested on publicly available fundus images and the results are compared. The accuracy of these algorithms is evaluated by sensitivity and specificity. The sensitivity and specificity for these algorithms are found to be very favorable.

Keywords

Glaucoma risk index cup to disc ratio multi-thresholding active contours region growing segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Youssif, A.A.-H.A.-R., Ghalwash, A.Z., Ghoneim, A.A.S.A.-R.: Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Transactions on Medical Imaging 27, 11–18 (2008)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Bock, R., Meier, J., Nyúl, L.G., Hornegger, J., Michelson, G.: Glaucoma risk index: Automated glaucoma detection from color fundus images. Medical Image Analysis 14, 471–481 (2010)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and haussdroff based template matching. IEEE Transactions on Medical Imaging 11, 1193–1200 (2001)CrossRefGoogle Scholar
  6. 6.
    Ghafar, R.A.A., Morris, T., Ritchings, T., Wood, I.: Detection and characterization of the optic disc in glaucoma and diabetic retinopathy. In: Medical Image Understand Annual Conference, London, UK, pp. 23–24 (September 2004)Google Scholar
  7. 7.
  8. 8.
    Banerjee, B., Bhattacharjee, T., Chowdhury, N.: Color Image Segmentation Technique Using Natural Grouping of Pixels. International Journal of Image Processing (IJIP) 4(4), 320–328 (2010)Google Scholar
  9. 9.
    Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transaction on Medical Imaging 19, 203–210 (2000)CrossRefGoogle Scholar
  10. 10.
    Rangayyan, R.M., Zhu, X., Ells, A.L.: Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles. Journal of Digital Imaging, 132–140 (February 2009)Google Scholar
  11. 11.
    Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transaction on Medical Imaging 25, 1200–1213 (2006)CrossRefGoogle Scholar
  12. 12.
    Salvatelli, A., Bizai, G., Barbosa, G., Drozdowicz, B., Delrieux, C.: A comparative analysis of pre-processing techniques in color retinal Images. In: 16th Argentine Bioengineering Congress and the 5th Conference of Clinical Engineering. IOP Publishing Journal of Physics: Conference series, vol. 90 (2007)Google Scholar
  13. 13.
    Kubecka, L., Jan, J., Kolar, R.: Retrospective Illumination Correction of Retinal Images. International Journal of Biomedical Imaging 2010, Article ID 780262, 10 pages (2010)CrossRefGoogle Scholar
  14. 14.
    Vanajakshi, B., Sujatha, B., Srirama, K.: A Study on Implementation of Advanced Morphological Operations. IJCSNS International Journal of Computer Science and Network Security 10(3), 6–9 (2010)Google Scholar
  15. 15.
    http://www.optic-disc.org/tutorials/glaucoma evaluation basics/page13.html
  16. 16.
    Criminisi, A., Pérez, P., Toyama, K.: Region Filling and Object Removal by Exemplar-Based Image Inpainting. IEEE Transactions on Image Processing 13(9) (2004)Google Scholar
  17. 17.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, New Orleans, USA, pp. 417–424 (2000)Google Scholar
  18. 18.
    Meier, J., Bock, R., Michelson, G., Nyúl, L.G., Hornegger, J.: Effects of Preprocessing Eye Fundus Images on Appearance Based Glaucoma Classification. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 165–172. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefzbMATHGoogle Scholar
  20. 20.
    Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transaction on Image Processing 10(2) (February 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mishra Madhusudhan
    • 1
  • Nath Malay
    • 1
  • S. R. Nirmala
    • 1
  • Dandapat Samerendra
    • 1
  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

Personalised recommendations