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

Katedra Informatyki Stosowanej (RAU/RAU7)

Silesian University of Technology
Friedrich-Alexander University Erlangen-Nürnberg

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Rozdział › recenzja

Abstrakt

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.

Język oryginału	angielski
Tytuł publikacji goszczącej	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Redaktorzy	Marian Bubak, Geert Dick van Albada, Peter M. A. Sloot, Jack J. Dongarra
Wydawca	Springer Verlag
Strony	49-55
Liczba stron	7
ISBN (drukowany)	3540221298
Identyfikatory DOI	https://doi.org/10.1007/978-3-540-25944-2_7
Status publikacji	Opublikowano - 2004

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Tom	3039
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

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

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Cytowanie

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

Sta̧por, Katarzyna ; Pawlaczyk, Leslaw ; Chrastek, Radim i in. / 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). redaktor / Marian Bubak ; Geert Dick van Albada ; Peter M. A. Sloot ; Jack J. Dongarra. Springer Verlag, 2004. str. 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.",

author = "Katarzyna St{\c a}por and Leslaw Pawlaczyk and Radim Chrastek and Georg Michelson",

year = "2004",

doi = "10.1007/978-3-540-25944-2\_7",

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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. w M Bubak, GD van Albada, PMA Sloot & JJ Dongarra (red), 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), tom 3039, Springer Verlag, str. 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 i in.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). red. / Marian Bubak; Geert Dick van Albada; Peter M. A. Sloot; Jack J. Dongarra. Springer Verlag, 2004. str. 49-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 3039).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Rozdział › recenzja

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

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Sta̧por K, Pawlaczyk L, Chrastek R, Michelson G. Automatic detection of glaucomatous changes using adaptive thresholding and neural networks. W Bubak M, van Albada GD, Sloot PMA, Dongarra JJ, redaktorzy, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. str. 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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Obszary tematyczne ASJC Scopus

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