Kernel K-Means clustering algorithm for identification of glaucoma in ophthalmology

• Autorzy: Stapor K. , Bruckner A.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

clustering; image segmentation; kernel-based learning

PL

grupowanie; segmentacja obrazu

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2005
• Tom: Vol. 9
• Strony: 167--172
• Opis fizyczny: Bibliogr. 6 poz., rys.

Twórcy

autor

Stapor K.

• Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

autor

Bruckner A.

• 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.