Abstract
In this paper a contribution towards diabetic damage detection in retinal images is proposed by synthesizing a Sparsely Connected Neurofuzzy Network for fundus image processing in the presence of retinopathies. A Hopfield-like neurofuzzy subnetwork is firstly synthesized to obtain contrast-enhanced images. After an optimal thresholding performed by an MLP-based neural subsystem, contrast-enhanced images are then globally segmented by a further sparsely-connected neural subnet to highlight vague pale regions. In this way diabetic damaged areas reveal isolated in bipolar output images. Experimental cases are reported and discussed.
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
Hsu, W., Pallawala, P.M.D.S., Li, L.M., Eong, K.A.: The Role of Domain Knowledge in the Detection of Retinal Hard Exudates. In: IEEE Computer Society Conf. on Computer Vision & Pattern Recognition, vol. 2, pp. 246–251 (2001)
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), 1236–1243 (2002)
Sinthanayothin, C., Kongbunkiat, V., Phoojaruenchanachai, S., Singalavanija, A.: Automated Screening System for Diabetic Retinopathy. In: Int. Sym. on Image and Signal Processing and Analysis, pp. 915–920 (2003)
Ennett, C.M., Frize, M., Charrette, E.: Improvement and Automation of Artificial Neural Networks to Estimate Medical Outcomes. Med. Eng. & Phys. 26, 321–328 (2004)
Kavitha, D., Shenbaga, D.S.: Automatic Detection of Optic Disc and Exudates in Retinal Images. In: IEEE Int. Conf. on Intelligent Sensing and Information Processing, pp. 501–506 (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 J. 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 J. of Ophthalmology 87(10), 1220–1223 (2003)
Swiercz, W., Cios, K.J., Staley, K., Kurgan, L., Accurso, F., Sagel, S.: A New Synaptic Plasticity Rule for Networks of Spiking Neurons. IEEE Trans. on NN 17(1), 94–105 (2006)
Carnimeo, L.: Synthesis of Neural Associative Memories for Artificial Vision Systems by Fuzzy Image Segmentations. Artificial Intelligence Series: Advances in Neural Networks World. World Scient. Eng. Soc. Press, Danvers (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carnimeo, L. (2008). Diabetic Damage Detection in Retinal Images Via a Sparsely-Connected Neurofuzzy Network. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_141
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)