Abstract
In this paper, we present a novel approach to microaneurysm candidate extraction. To strengthen the accuracy of individual algorithms, we propose an ensemble of state-of-the-art candidate extractors. We apply a simulated annealing based method to select an optimal combination of such algorithms for a particular dataset. We also present a novel classification technique, which is based on a parallel ensemble of kernel density estimators. The experimental results show improvement in the positive likelihood rate compared to the individual candidate extractors.
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Harding, S., Greenwood, R., Aldington, S., Gibson, J., Owens, D., Taylor, R., Kohner, E., Scanlon, P., Leese, G.: Grading and disease management in national screening for diabetic retinopathy in england and wales. Diabetic Medicine 20, 965–971 (2003)
UK National Screening Committee: National Screening Programme for Diabetic Retinopathy (2009), http://www.retinalscreening.nhs.uk/
Abramoff, M., Niemeijer, M., Suttorp-Schulten, M., Viergever, M.A., Russel, S.R., van Ginneken, B.: Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31, 193–198 (2008)
Hejlesen, O., Ege, B., Englemeier, K.H., Aldington, S., McCanna, L., Bek, T.: Tosca-imaging developing internet based image processing software for screening and diagnosis of diabetic retinopathy. In: MEDINFO 2004, pp. 222–226 (2004)
Niemeijer, M., van Ginneken, B., Cree, M., Mizutani, A., Quellec, G., Sanchez, C., Zhang, B., Hornero, R., Lamard, M., Muramatsu, C., Wu, X., Cazuguel, G., You, J., Mayo, A., Li, Q., Hatanaka, Y., Cochener, B., Roux, C., Karray, F., Garcia, M., Fujita, H., Abramoff, M.: Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs. IEEE Transactions on Medical Imaging 29, 185–195 (2010)
Walter, T., Massin, P., Arginay, A., Ordonez, R., Jeulin, C., Klein, J.C.: Automatic detection of microaneurysms in color fundus images. Medical Image Analysis 11, 555–566 (2007)
Spencer, T., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers and Biomedical Research 29, 284–302 (1996)
Frame, A.J., Undrill, P.E., Cree, M.J., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.: A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Computers in Biology and Medicine 28, 225–238 (1998)
Abdelazeem, S.: Microaneurysm detection using vessels removal and circular hough transform. In: Proceedings of the Nineteenth National Radio Science Conference, pp. 421–426 (2002)
Niemeijer, M., Staal, J., Abramoff, M.D., Suttorp-Schulten, M.A., van Ginneken, B.: Automatic detection of red lesions in digital color fundus photographs. IEEE Transactions on Medical Imaging 24, 584–592 (2005)
Mizutani, A., Muramatsua, C., Hatanakab, Y., Suemoria, S., Haraa, T., Fujita, H.: Automated microaneurysm detection method based on double-ring filter in retinal fundus images. In: Medical Imaging 2009: Computer-Aided Diagnosis. Proceedings of SPIE, vol. 7260, pp. 1N1–1N8 (2009)
Fleming, A.D., Philip, S., Goatman, K.A.: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Transactions on Medical Imaging 25(9), 1223–1232 (2006)
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV, pp. 474–485 (1994)
Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. The Journal of VLSI Signal Processing 38, 35–44 (2004)
Chen, T.C., Chung, K.L.: An efficient randomized algorithm for detecting circles. Computer Vision and Image Understanding 83, 172–191 (2001)
Lazar, I., Antal, B., Hajdu, A.: Microaneurysm detection in digital fundus images. Technical Report 2010/14(387), University of Debrecen, Hungary (2010)
Johnson, N.P.: Advantages to transforming the receiver operating characteristic (roc) curve into likelihood ratio co-ordinates. Stastics in Medicine 23, 2257–2266 (2004)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Zhang, B., Wu, X., You, J., Li, Q., Karray, F.: Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recogn. 43, 2237–2248 (2010)
Cree, M.J., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: A fully automated comparative microaneurysm digital detection system. Eye 11, 622–628 (1997)
Sondberg-madsen, N., Thomsen, C., Pena, J.M.: Unsupervised feature subset selection. In: Proceedings of the Workshop on Probabilistic Graphical Models for Classification, pp. 71–82 (2003)
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Antal, B., Lázár, I., Hajdu, A. (2012). An Ensemble Approach to Improve Microaneurysm Candidate Extraction. In: Obaidat, M.S., Tsihrintzis, G.A., Filipe, J. (eds) e-Business and Telecommunications. ICETE 2010. Communications in Computer and Information Science, vol 222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25206-8_25
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DOI: https://doi.org/10.1007/978-3-642-25206-8_25
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