Skip to main content
SpringerLink
Log in

The Modelling of Glaucoma Progression through the Use of Cellular Automata

  • Conference paper
Advances in Intelligent Data Analysis XII (IDA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8207))

Included in the following conference series:

Abstract

We propose a model of glaucoma progression based on the application of Cellular Automata (CA) to visual field (VF) data, obtained through automated perimetry. VF sensitivities are converted into ganglion cell loss and CA are utilised to model the gradual deterioration of vision, mimicking degeneration of the actual ganglia. First we discuss the construction of a grid that approximates the VF map and the corresponding layer of ganglia in terms of cell counts in individual fields. The grid is populated with dead cells in accordance with patients’ tests, and then we run a CA, utilising a majority and a probabilistic rule. Preliminary results are presented, showing that during its evolution, the CA often converges to configurations where the death of cells resembles VF data of the same patients, at later time. That is, the percentage loss of cells in VF fields observed in the CA resembles the real VF data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Quingley, H.A.: Glaucoma. Lancet 377(9774), 1367–1377 (2011), doi:10.1016/S0140-6736(10)61423-7

    Article  Google Scholar 

  2. Pascolini, D., Mariotti, S.P.: Global estimates of visual impairment: 2010. Br. J. Ophthalmol (December 2011), doi:10.1136/bjophthalmol-2011-300539

    Google Scholar 

  3. Sharma, P., Sample, P.A., Zangwill, L.M., Schuman, J.S.: Diagnostic tools for glaucoma detection and management. Surv. Ophthalmol. 53(suppl. 1), S17–S7 (2008), doi:10.1016/j.survophthal.2008.08.003

    Google Scholar 

  4. Fitzke, F.W., Hitchings, R.A., Poinoosawmy, D., McNaught, A.I., Crabb, D.P.: Analysis of visual field progression in glaucoma. Br. J. Ophthalmol. 80(1), 40–48 (1996)

    Article  Google Scholar 

  5. Heijl, A., Lindgren, G., Lindgren, A.: Extended empirical statistical package for evaluation of single and multiple fields in glaucoma: Statpac 2. In: Mills, R., Heijl, A. (eds.) Perimetry Update 1990/1, pp. 303–315. Kluger Publications (1990)

    Google Scholar 

  6. Swift, S., Liu, X.: Predicting glaucomatous visual field deterioration through short multivariate time series modelling. Artif. Intell. Med. 24(1), 5–24 (2002)

    Article  Google Scholar 

  7. Tucker, A., Vinciotti, V., Liu, X., Garway-Heat, D.: A spatio-temporal Bayesian network classifier for understanding visual field deterioration. Artif. Intell. Med 34(2), 163–177 (2005)

    Article  Google Scholar 

  8. Sacchi, L., Tucker, A., Counsell, S., Garway-Heath, D., Swift, S.: Understanding Glaucoma Progression Using Temporal Abstractions and Association Rules. In: Proceedings of the Annual Workshop on Intelligent Data Analysis in Biomedicine and Pharmacology, IDAMAP, Lake Bled, Slovenia (July 06, 2011)

    Google Scholar 

  9. Lan, Y.N., Henson, D.B., Kwartz, A.J.: The correlation between optic nerve head topographic measurements, peripapillary nerve fibre layer thickness, and visual field indices in glaucoma. Br. J. Ophthalmol. 87(9), 1135–1141 (2003)

    Article  Google Scholar 

  10. Quigley, H.A.: Neuronal death in glaucoma. Prog. Retin. Eye. Res. 18(1), 39–57 (1999)

    Article  Google Scholar 

  11. Quigley, H.A., Dunkelberger, G.R., Green, W.R.: Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma. American Journal of Ophthalmology 107(5), 453–464 (1989)

    Google Scholar 

  12. Harwerth, R.S., Carter-Dawson, L., Smith III, E.L., Barnes, G., Holt, W.F., Crawford, M.L.: Neural losses correlated with visual losses inclinical perimetry. Invest. Ophthalmol. Vis. Sci. 45(9), 3152–3160 (2004)

    Article  Google Scholar 

  13. Harwerth, R.S., Quigley, H.A.: Visual field defects and retinal ganglion cell losses in patients with glaucoma. Arch. Ophthalmol. 124(6), 853–859 (2006)

    Article  Google Scholar 

  14. Swanson, W.H., Felius, J., Pan, F.: Perimetric defects and ganglion cell damage: interpreting linear relations using a two-stage neural model. Invest. OphthalmolVis. Sci. 45(2), 466–472 (2004)

    Article  Google Scholar 

  15. Drasdo, N., Mortlock, K.E., North, R.V.: Ganglion Cell Loss and Dysfunction: Relationship to Perimetric Sensitivity. Optom Vis. Sci 85(11), 1036–1042 (2008), doi:10.1097/OPX.0b013e31818b94af

    Article  Google Scholar 

  16. Shaarawy, T., Sherwood, M.B., Crowston, J.G., Hitchings, R.A.: Glaucoma: Medical Diagnosis & Therapy, vol. 1. Saunders Ltd. (2009)

    Google Scholar 

  17. Hecht, E.: Optics, 3rd edn. Addison Wesley (1997)

    Google Scholar 

  18. Sjöstrand, J., Olsson, V., Popovic, Z., Conradi, N.: Quantitative estimations of foveal and extra-foveal retinal circuitry in humans. Vision Res. 39(18), 2987–2998 (1999)

    Article  Google Scholar 

  19. Lei, Y., Garrahan, N., Hermann, B., Fautsch, M.P., Johnson, D.H., Hernandez, M.R., Boulton, M., Morgan, J.E.: Topography of neuron loss in the retinal ganglion cell layer in. Br. J. Ophthalmol. 93, 1676–1679 (2009), doi:10.1136/bjo.2009.159210

    Article  Google Scholar 

  20. Neufeld, A.H.: Nitric oxide: a potential mediator of retinal ganglion cell damage in glaucoma. Surv. Ophthalmol. 43(suppl. 1), S129–S135 (1999)

    Google Scholar 

  21. Levkovitch-Verbin, H., Quigley, H.A., Martin, K.R., Zack, D.J., Pease, M.E., Valenta, D.F.: A model to study differences between primary and secondary degeneration of retinal ganglion cells in rats by partial optic nerve transection. Invest Ophthalmol. Vis. Sci. 44, 3388–3393 (2003)

    Article  Google Scholar 

  22. Bandini, S.: Cellular Automata. Future Generation Computer Systems, vol. 18 (2002)

    Google Scholar 

  23. Drasdo, N., Millican, C.L., Katholim, C.R., Curcio, C.A.: The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field. Vision Res. 47(22), 2901–2911 (2007)

    Article  Google Scholar 

  24. Drasdo, N.: Receptive field densities of the ganglion cells of the human retina. Vision Res. 29(8), 985–988 (1989)

    Article  Google Scholar 

  25. Snyder, A.W., Miller, W.H.: Photoreceptor diameter and spacing for highest resolving of gratings. J. Opt. Soc. Am. 67, 696–698 (1977)

    Article  Google Scholar 

  26. Michalewicz, Z., Fogel, D.B.: How to solve it: Modern heuristics. Springer, Berlin (1998)

    Google Scholar 

  27. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann. Arbor (1975)

    Google Scholar 

  28. Heijl, A.: Concept and Importance of Visual Field Measurements to Detect Glaucoma Progression. Glaucoma Now 2, 2–4 (2010)

    Google Scholar 

  29. Skrobanski, S., Pavlidis, S., Ismail, W., Hassan, R., Counsell, S., Swift, S.: Use of General Purpose GPU Programming to Enhance the Classification of Leukaemia Blast Cells in Blood Smear Images. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 369–380. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems and Computing, Brunel University, London, UK

    Stelios Pavlidis, Stephen Swift, Allan Tucker & Steve Counsell

Authors
  1. Stelios Pavlidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Swift
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allan Tucker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steve Counsell
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information Systems, Computing and Mathematics, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK

    Allan Tucker  & Stephen Swift  & 

  2. Faculty of Computer Science/IT, Ostfalia University of Applied Sciences, Am Exer 2, 38302, WolfenbĂĽttel, Germany

    Frank Höppner

  3. Faculty of Science, Department of Information and Computing Science, Buys Ballot Laboratory, Universiteit Utrecht, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Arno Siebes

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pavlidis, S., Swift, S., Tucker, A., Counsell, S. (2013). The Modelling of Glaucoma Progression through the Use of Cellular Automata. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41398-8_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41397-1

  • Online ISBN: 978-3-642-41398-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation