Abstract
Diabetic retinopathy is a common eye disease directly associated with diabetes and one of the leading causes for blindness. One of its early indicators is the presence of exudates on the retina. In this paper we present a neural network-based approach to automatically detect exudates in retina images. A sliding windowing technique is used to extract parts of the image which are then passed to the neural net to classify whether the area is part of an exudate region or not. Principal component analysis and histogram specification are used to reduce training times and complexity of the network, and to improve the classification rate. Experimental results on an image data set with known exudate locations show good performance with a sensitivity of 94.78% and a specificity of 94.29%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mayer-Base, A.: Pattern Recognition for Medical Imaging. Elsevier, USA (2004)
Kanski, J.J.: Clinical Ophthalmology. Great Britain: Reed Education and Professional Publishing (1999)
Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research 25, 99–127 (2006)
Gardner, G.G., Keating, D., Williamson, T.H., Elliott, A.T.: Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. British Journal of Ophthalmology 80, 940–944 (1996)
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology 87, 1220–1223 (2003)
Walter, T., Klein, J.-C., Massin, P., Erginay, A.A: Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy – Detection of Exudates in Color Fundus Images of the Human Retina. IEEE Trans. On Medical Imaging 21(10) (2002)
Sinthanayothin, C., Boyce, J., Williamson, T., Cook, H., Mensah, E., Lal, S., Usher, D.: Automated Detection of Diabetic Retinopathy on Digital Fundus Images. Diabetic Medicine 21(1), 84–90 (2004)
Goatmana, K.A., Whitwama, A.D., Manivannana, A., Olsonb, J.A, Sharpa, P.F.: Colour Normalisation of Retinal Images. In: Proc. Medical Image Understanding and Analysis (2003)
Nabney, I.T.: Netlab: Algorithms for Pattern Recognition. Springer, Great Britain (2002)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, New Jersey (2002)
Finlayson, G., Hordley, S., Schaefer, G., Tian, G.-Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38, 179–190 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schaefer, G., Leung, E. (2007). Neural Networks for Exudate Detection in Retinal Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-76856-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
Online ISBN: 978-3-540-76856-2
eBook Packages: Computer ScienceComputer Science (R0)