Automatic detection of glaucomatous changes using adaptive thresholding and neural networks

Katarzyna Sta̧por; Leslaw Pawlaczyk; Radim Chrastek; Georg Michelson

doi:10.1007/978-3-540-25944-2_7

Automatic detection of glaucomatous changes using adaptive thresholding and neural networks

Katarzyna Sta̧por
, Leslaw Pawlaczyk
, Radim Chrastek
, Georg Michelson

Department of Applied Informatics (RAU/RAU7)

Silesian University of Technology
Friedrich-Alexander University Erlangen-Nürnberg

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Marian Bubak, Geert Dick van Albada, Peter M. A. Sloot, Jack J. Dongarra
Publisher	Springer Verlag
Pages	49-55
Number of pages	7
ISBN (Print)	3540221298
DOIs	https://doi.org/10.1007/978-3-540-25944-2_7
Publication status	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3039
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-25944-2_7

Cite this

Sta̧por, K., Pawlaczyk, L., Chrastek, R., & Michelson, G. (2004). Automatic detection of glaucomatous changes using adaptive thresholding and neural networks. In M. Bubak, G. D. van Albada, P. M. A. Sloot, & J. J. Dongarra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 49-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3039). Springer Verlag. https://doi.org/10.1007/978-3-540-25944-2_7

Sta̧por, Katarzyna ; Pawlaczyk, Leslaw ; Chrastek, Radim et al. / Automatic detection of glaucomatous changes using adaptive thresholding and neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Marian Bubak ; Geert Dick van Albada ; Peter M. A. Sloot ; Jack J. Dongarra. Springer Verlag, 2004. pp. 49-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic detection of glaucomatous changes using adaptive thresholding and neural networks",

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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editor = "Marian Bubak and \{van Albada\}, \{Geert Dick\} and Sloot, \{Peter M. A.\} and Dongarra, \{Jack J.\}",

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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 M Bubak, GD van Albada, PMA Sloot & JJ Dongarra (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3039, Springer Verlag, pp. 49-55. https://doi.org/10.1007/978-3-540-25944-2_7

Automatic detection of glaucomatous changes using adaptive thresholding and neural networks. / Sta̧por, Katarzyna; Pawlaczyk, Leslaw; Chrastek, Radim et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Marian Bubak; Geert Dick van Albada; Peter M. A. Sloot; Jack J. Dongarra. Springer Verlag, 2004. p. 49-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3039).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Automatic detection of glaucomatous changes using adaptive thresholding and neural networks

AU - Sta̧por, Katarzyna

AU - Pawlaczyk, Leslaw

AU - Chrastek, Radim

AU - Michelson, Georg

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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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DO - 10.1007/978-3-540-25944-2_7

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SN - 3540221298

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Bubak, Marian

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A2 - Sloot, Peter M. A.

A2 - Dongarra, Jack J.

PB - Springer Verlag

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Sta̧por K, Pawlaczyk L, Chrastek R, Michelson G. Automatic detection of glaucomatous changes using adaptive thresholding and neural networks. In Bubak M, van Albada GD, Sloot PMA, Dongarra JJ, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 49-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-25944-2_7

Automatic detection of glaucomatous changes using adaptive thresholding and neural networks

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