Abstract
In this paper the new method for automatic classification of fundus eye images into normal and glaucomatous ones is proposed. The new, morphological features for quantitative cup evaluation are proposed based on genetic algorithms. For computation of these features the original method for automatic segmentation of the cup contour is proposed. The computed features are then used in classification procedure which is based on multilayer perceptron. The mean sensitivity is 90%, while the mean specificity: 86%. The obtained results are encouraging.
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© 2004 Springer-Verlag Berlin Heidelberg
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Sta̧por, K., Pawlaczyk, L., Chrastek, R., Michelson, G. (2004). Automatic Detection of Glaucomatous Changes Using Adaptive Thresholding and Neural Networks. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25944-2_7
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DOI: https://doi.org/10.1007/978-3-540-25944-2_7
Publisher Name: Springer, Berlin, Heidelberg
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