Abstract
Two new computer diagnostic methods for the automatic determination of glaucoma diagnosis are presented and compared in this paper: the statistical method and the neural net method. Both introduced methods evaluate colour glaucomatous changes within the optic disc area. The mentioned colour changes are numerically represented using a suitable image analysis process. Next, the investigated eye is classified to three defined glaucoma-risk classes with different reliability of the diagnosis determination. The verification and the reliability comparison of both studied methods are performed by virtue of the application of this methods to a set of normal healthy optic disc images and a set of glaucomatous optic disc images.
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References
B. Shields: Textbook of Glaucoma, Williams & Wilkins, Baltimore, 1992.
K. Hanuš, J. Boguszaková, et al. Compendium of Eye Medicine, Grada, Praha, 1997.
A. P. Nesterov: Primary Glaucoma, Avicenum, Praha, 1991.
S. Řehák, R. Knobloch, et al. Medicine of the Eye, Avicenum, Praha, 1980.
P. Nagin, B. Schwartz: “Detection of increased pallor over time. Computerized image analysis in untreated ocular hypertension”, Ophthalmology, Vol. 92, (1985), pp. 252–261.
T.N. Cornsweet, S. Hersh, et al: “Quantification of the shape and color of the optic nerve head”, In: G.M. Breinin and I.M. Siegel (Ed.): Advances in Diagnostics Visual Optics, Springer-Verlag, New York, 1983, pp. 141–149.
R. Varma, G.L. Spaeth: “The PAR IS 2002: A new system for retinal digital image analysis”, Ophthalmic Surgery, Vol. 19, (1988), pp. 183–192.
A. Sugiyama, T. Moriyama, H. Ichiki: Retinal disease analyzer, U.S. Patent No. 5868134, U.S. Government, Washington, 1999.
F.S. Mikelberg, K. Wijsman, M. Schulzer: “Reproducibility of topographic parameters obtained with the heidelberg retina tomograph”, J. of Glaucoma, Vol. 2., (1993), pp. 101–103.
G. Zinser, U. Harbarth, H. Schröder: “Formation and analysis of three-dimensional data with the laser tomographic scanner (LST)”, In: J.E. Nasemann and R.O.W. Burk (Eds.): Scanning Laser Ophthalmoscopy and Tomography, Quintessenz Verlag, München, 1990, pp. 243–252.
K.U. Bartz-Schmidt, A. Sengersdorf et al.: “The cumulative normalised rim/disc area ratio curve”, Greafe's Achive for Clinical and Experimental Ophthalmology, Vol. 234, (1995), pp. 227–231.
F. Pluháček, J. Pospíšil: “Present methods of the computer-image analysis of a human retina with regard to diagnostics of glaucoma”, Fine Mechanics and Optics, Vol. 46, (2001), pp. 326–334.
L. Kubáček, L. Kubáčková: Statistics and Metrology, Palacký University Press, Olomouc, 2000.
J. Škrášek, Z. Tichý: Principles of Applied Mathematics III, SNTL, Praha, 1990.
B. Müller, J. Reinhardt, M.T. Strickland: Neural Networks: An Introduction, Springer, Berlin, 1995.
J.G. Taylor: Neural Networks and Their Applications, John Wiley and Sons, Chichester, 1996.
L. Machala, J. Pospíšil: “Proposal and verification of two methods for evaluation of the human iris video-camera images”, Optik, Vol. 112, (2001), pp. 335–340.
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Pluháček, F., Pospíšil, J. Statistical and neural net methods for automatic glaucoma diagnosis determination. centr.eur.j.phys. 2, 12–24 (2004). https://doi.org/10.2478/BF02476270
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DOI: https://doi.org/10.2478/BF02476270