Abstract
In this paper the new method for the automatic segmentation and classification of fundus eye images taken from classical fundus camera into normal and glaucomatous ones is proposed. The presented method consists of the following three stages: segmentation, feature selection, and classification. The mean sensitivity of the proposed method is 93%, while the mean specificity is 97%.
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References
Fukunaga K, Hostetler LD (1975) The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans. On Information Theory, 21(1), 32–40
Goh KG, Hsu W, Lee M, Wang H (2000) ADRIS: an automatic diabetic retinal image screening system. In: Cios KJ (ed) Medical Data Mining and Knowledge Discovery. Springer-Verlag, New York, 181–210
Goldberg D (1989) Genetic algorithms in search optimization and machine learning. Addison Wesley
Gonzalez RC, Woods R.E (2002) Digital image processing. Prentice-Hall, New Jersey
Jain AK, Dubes RC. (1988) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs N.J.
Kanski J, McAllister JA, Salmon JF, Tarrant TR (1996) Glaucoma: a color manual of diagnosis and treatment. Butterworth-Heinemann Medical
Morris DT, Donnison C (1999) Identifying the neuroretinal rim boundary using dynamic contours. Image and Vision Computing. 17(3–4): 169–174
Pinz A, Prantl M, Datlinger P (1998) Mapping the human retina. IEEE Trans. Medical Imaging, 17(4): 210–215
Sinthanayothin C, Boyce J, Williamson CT (1999) Automated localization of the optic disk, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology. 38(1): 902–910
Stapor K, Switonski A (2004) Automatic analysis of fundus eye images using mathematical morphology and neural networks for supporting glaucoma diagnosis. Machine Graphics & Vision, 13(1/2): 65–79
Trier O, Jain A, Taxt T (1996) Feature extraction methods for character recognition — a survey. Pattern Recognition. 29(4): 641–662
Vapnik V (1995) The nature of statistical learning theory. Springer Verlag, New York
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© 2005 Springer-Verlag Berlin Heidelberg
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Stapor, K., Brueckner, A. (2005). Mean Shift Segmentation, Genetic Algorithms and Support Vector Machines for Identification of Glaucoma in Fundus Eye Images. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_80
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DOI: https://doi.org/10.1007/3-540-32390-2_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
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