Abstract
We present an approach of automatically detecting exudates in retinal images using neural networks. Exudates are one of the early indicators of diabetic retinopathy which is known as one of the leading causes for blindness. A neural network is trained to classify whether small image windows are part of exudate areas or not. Furthermore, it is shown that a pre-processing step based on histogram specification in order to deal with varying lighting conditions greatly improves the recognition performance. Application of principal component analysis is used for dimensionality reduction and speed-up of the system. Experimental results were obtained on an image data set with known exudate locations and showed good classification 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.
Similar content being viewed by others
References
Aiello, L., Callerano, J., Gardner, T., King, D., Blankenship, G., Ferris, F., Klein, R.: Diabetic retinopathy. Diabetes Care 21, 143–156 (1998)
Finlayson, G., Hordley, S., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38, 179–190 (2005)
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(11), 940–944 (1996)
Goatman, K.A., Whitwam, A.D., Manivannan, A., Olson, J.A., Sharp, P.F.: Colour normalisation of retinal images. In: Medical Image Understanding and Analysis (2003)
Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison Wesley, Reading (1992)
Nabney, I.T.: Netlab: Algorithms for Pattern Recognition. Springer, Heidelberg (2002)
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology 87(10), 1220–1223 (2003)
Patton, N., Aslam, T.M., MacGillvray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesam, K., Constable, I.J.: Retinal image analysis: concepts, applications and potential. Progress in Retinal and Eye Research 25(1), 99–127 (2006)
Sinthanayothin, C., Boyce, J.F., Williamson, T.H., Cook, H.L., Mensah, E., Lal, S., Usher, D.: Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine 19(2), 105–112 (2002)
Walter, T., Klein, J., 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. Medical Imaging 21(10), 1236–1243 (2002)
Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39(4), 561–577 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schaefer, G., Leung, E. (2009). An Investigation into Neural Networks for the Detection of Exudates in Retinal Images. In: Avineri, E., Köppen, M., Dahal, K., Sunitiyoso, Y., Roy, R. (eds) Applications of Soft Computing. Advances in Soft Computing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88079-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-88079-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88078-3
Online ISBN: 978-3-540-88079-0
eBook Packages: EngineeringEngineering (R0)