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2004 | 2 | 1 | 12-24
Tytuł artykułu

Statistical and neural net methods for automatic glaucoma diagnosis determination

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca

Czasopismo
Rocznik
Tom
2
Numer
1
Strony
12-24
Opis fizyczny
Daty
wydano
2004-03-01
online
2004-03-01
Twórcy
  • Department of Experimental Physics and Joint Laboratory of Optics of Palacký, University and Institute of Physics of Academy of Sciences, 17 listopadu 50a, 772 07, Olomouc, Czech Republic, pluhacek@prfnw.upol.cz
  • Department of Experimental Physics and Joint Laboratory of Optics of Palacký, University and Institute of Physics of Academy of Sciences, 17 listopadu 50a, 772 07, Olomouc, Czech Republic, jpospis@risc.upol.cz
Bibliografia
  • [1] B. Shields: Textbook of Glaucoma, Williams & Wilkins, Baltimore, 1992.
  • [2] K. Hanuš, J. Boguszaková, et al. Compendium of Eye Medicine, Grada, Praha, 1997.
  • [3] A. P. Nesterov: Primary Glaucoma, Avicenum, Praha, 1991.
  • [4] S. Řehák, R. Knobloch, et al. Medicine of the Eye, Avicenum, Praha, 1980.
  • [5] P. Nagin, B. Schwartz: “Detection of increased pallor over time. Computerized image analysis in untreated ocular hypertension”, Ophthalmology, Vol. 92, (1985), pp. 252–261.
  • [6] 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.
  • [7] 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.
  • [8] A. Sugiyama, T. Moriyama, H. Ichiki: Retinal disease analyzer, U.S. Patent No. 5868134, U.S. Government, Washington, 1999.
  • [9] 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. [PubMed]
  • [10] 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.
  • [11] 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. http://dx.doi.org/10.1007/BF00430414[Crossref]
  • [12] 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.
  • [13] L. Kubáček, L. Kubáčková: Statistics and Metrology, Palacký University Press, Olomouc, 2000.
  • [14] J. Škrášek, Z. Tichý: Principles of Applied Mathematics III, SNTL, Praha, 1990.
  • [15] B. Müller, J. Reinhardt, M.T. Strickland: Neural Networks: An Introduction, Springer, Berlin, 1995.
  • [16] J.G. Taylor: Neural Networks and Their Applications, John Wiley and Sons, Chichester, 1996.
  • [17] 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_BF02476270
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