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Języki publikacji
Abstrakty
This paper presents the improved version of the classification system for supporting glaucoma diagnosis in ophthalmology, proposed in [4]. In this paper we propose the new segmentation step based on the kernel K-Means clustering algorithm which enable for better classification performance.
Rocznik
Tom
Strony
167--172
Opis fizyczny
Bibliogr. 6 poz., rys.
Twórcy
autor
- Institute of Computer Science, Silesian University of Technology, Gliwice, Poland
autor
- Institute of Mathematics, Silesian University, Katowice, Poland
Bibliografia
- [1] JAIN A.K., DUBES R.C., Algorithms for clustering data, Prentice-Hall, Englewood Cliffs N.J, 1988.
- [2] KANSKI J., McALLISTER J.A., SALMON J.F., TARRANT T.R., Glaucoma: a color manual of diagnosis and treatment, Butterworth–Heinemann Medical, 1996.
- [3] MUELLER C.R., et al., An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks, 12(2), 2001.
- [4] STAPOR K., SWITONSKI A., Automatic analysis of fundus eye images using mathematical morphology and neural networks for supporting glaucoma diagnosis, Machine Graphics & Vision, 13(1/2), 65–79, 2004.
- [5] STAPOR K, Automatic classification of objects. EXIT, Warszawa 2005. (in Polish)
- [6] VAPNIK V., The nature of statistical learning theory, Springer Verlag, New York, 1995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0012-0018