Support vector clustering algorithm for identification of glaucoma in ophthalmology

• Autorzy: Stąpor K.
• Języki publikacji: EN

Abstrakty

EN

This paper presents the improved version of the classification system for supporting glaucoma diagnosis in ophthalmology. In this paper we propose the new segmentation step based on the support vector clustering algorithm which enables better classification performance. Key words: clustering, image segmentation, kernel-based learning, glaucoma.

Słowa kluczowe

EN

clustering; image segmentation; kernel-based learning; glaucoma

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2006
• Tom: Vol. 54, nr 1
• Strony: 139--141
• Opis fizyczny: Bibliogr. 7 poz., 1 rys.

Twórcy

autor

Stąpor K.

• Institute of Computer Science, Silesian Technical University, 16 Akademicka St., 44-100 Gliwice, Poland,

Bibliografia

• [1] K. Stapor and A. Switonski, "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).
• [2] J. Kanski, et al., Glaucoma: a Color Manual of Diagnosis and Treatment, Butterworth-Heinemann Medical, 1996.
• [3] A. Ben-Hur et al, "Support vector clustering", J. Machine Learning Research 2, 125-137 (2001).
• [4] C.R. Mueller, et al., "An introduction to kernel-based learning algorithms", IEEE Trans. Neural Networks 12 (2), 2001.
• [5] V. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995.
• [6] K. Stąpor, Automatic Classification of Objects, Exit, Warszawa, 2005, (in Polish).
• [7] A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, N.J., 1988.