Abstract
Age-related macular degeneration (AMD) is the leading cause of visual deficiency and irreversible blindness for elderly individuals in Western countries. Its screening relies on human analysis of fundus images which often leads to inter- and intra-expert variability. With the aim of developing an automatic grading system for AMD, this paper focuses on identifying the best features for automatic detection of AMD in fundus images. First, different features based on local binary pattern (LBP), run-length matrix, color or gradient information are computed. Then, a feature selection is applied for dimensionality reduction. Finally, a support vector machine is trained to determine the presence or absence of AMD. Experiments were conducted on a dataset of 140 fundus images. A classification performance with an accuracy of 96 % is achieved on preprocessed images of macula area using LBP features.
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References
Kasuga, D.T., Chen, Y., Zhang, K.: Genetics of age-related degeneration. In: Ho, C.A., Regillo, C.D. (eds.) Age-related Macular Degeneration Diagnosis and Treatment, pp. 1–14 (2011)
Davis, M.D., Gangnon, R.E., Lee, L.Y., Hubbard, L.D., Klein, B.E., Klein, R., Ferris, F.L., Bressler, S.B., Milton, R.C.: The age-related eye disease study severity scale for age-related macular degeneration. Arch. Ophtalmol. 123, 1484–1498 (2005)
van Grinsven, M.J., Lechanteur, Y.T., van de Ven, J.P., van Ginneken, B., Hoyng, C.B., Theelen, T., Sánchez, C.I.: Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images. Invest. Ophthalmol. Vis. Sci. 54(4), 3019–3027 (2013)
Kankanahalli, S., Burlina, P.M., Wolfson, Y., Freund, D.E., Bressler, N.M.: Automated classification of severity of age-related macular degeneration from fundus photographs. Invest. Ophthalmol. Vis. Sci. 54(3), 1789–1796 (2013)
Garnier, M., Hurtut, T., Tahar, H.B., Cheriet, F.: Automatic multiresolution age-related macular degeneration detection from fundus images. In: SPIE Medical Imaging, International Society for Optics and Photonics. pp. 903532-903532 (2014)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Guo, Z., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Tang, X.: Texture information in run-length matrices. IEEE Trans. Image Process. 7(11), 1602–1609 (1998)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, vol. 1, pp. 886-893 (2005)
Pudil, P., Ferri, F.J., Novovicova, J., Kittler, J.: Floating search methods for feature selection with nonmonotonic criterion functions. In: Proceedings of the Twelveth International Conference on Pattern Recognition, IAPR (1994)
Steinwart, I., Christmann, A.: Support Vector Machines. Springer Science & Business Media, New York (2008)
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Phan, T.V., Seoud, L., Cheriet, F. (2015). Towards an Automatic Clinical Classification of Age-Related Macular Degeneration. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_38
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DOI: https://doi.org/10.1007/978-3-319-20801-5_38
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