The Diagnosis of Diabetic Retinopathy Disease via Vascular Structure Graph Matching

Year 2015, Volume: 36 Issue: 3, 209 - 215, 13.05.2015

Masoume Shokri , Siamak Haghipour

Abstract

Abstract. Diabetic retinopathy is a chronic retinal disorder that affects nearly all patients with diabetes mellitus, albeit with different severity. In this paper a new method based on graph matching is offered. Mentioned method has three main phases which in the first phase, entered image is preprocessed in order to become an image with uniform background and appropriate contrast. Then image is segmented by matched filter with Gaussian-Hermite kernel and retinal vessels will be extracted. In the second phase vascular structure graph is created with fractionating and in the last phase neural network is used for diagnosing normal images and images with signs of diabetic retinopathy. Images that are used for recommended method are derived from the ImageRet database. This offered method has appropriate accuracy for distinguishing healthy and unhealthy images.

Keywords

Diabetic Retinopathy, Matched Filter, Graph Matching, Neural Network, Retinal Images

References

• Klein, R., Meuer, S. M., Moss, S. E., & Klein, B. E. (1995) ,“ Retinal microaneurysm counts and 10-year progression of diabetic retinopathy,” Archives of Ophthalmology, 113(11), 1386-1391.
• Abu El-Asrar, A. M., Al-Mezaine, H. S., & Ola, M. S. (2009), “Pathophysiology and management of diabetic retinopathy,”
• Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., & Klein, J. C. (2007), “Automatic detection of microaneurysms in color fundus images,” Medical image analysis, 11(6), 555-566.
• Lay, B. (1983), “Analyse automatique des images angiofluorographiques au cours de la rétinopathie diabétique,” Ecole Nationale Supérieure des Mines de Paris, Centre de Morphologie Mathématique, Paris, France.
• Spencer, T., Olson, J. A., McHardy, K. C., Sharp, P. F., & Forrester, J. V. (1996) ,“ An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus,”Computers and biomedical research, 29(4), 284- 302.
• Kauppi, T., Kalesnykiene, V., Kamarainen, J. K., Lensu, L., Sorri, I., Raninen, A., ... & Pietilä, J. (2007) ,“ The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol,” In BMVC (pp. 1-10).
• Khan, M. I., Shaikh, H., Mansuri, A. M., & Soni, P. (2011),“ A Review of Retinal Vessel Segmentation Techniques and Algorithms,” International Journal of Computer Technology and Applications, 2(5).
• Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012) ,“ Blood vessel segmentation methodologies in retinal images–A survey,” Computer methods and programs in biomedicine, 108(1), 407-433.
• Wang, L., Bhalerao, A., & Wilson, R. (2007),“ Analysis of retinal vasculature using a multiresolution hermite model,” Medical Imaging, IEEE Transactions on, 26(2), 137-152.
• Goh, C. F. (2008), “0-1 graph partitioning and image segmentation,” (Doctoral dissertation, Massachusetts Institute of Technology).
• Eriksson, A. P., Barr, O., & Åström, K. (2006) ,“ Image segmentation using minimal graph cuts,” Proceedings SSBA, 45-48.
• Fjällström, P. O. (1998), “Algorithms for graph partitioning: A survey,” Linköping electronic articles in computer and information science, 3(10).
• Duarte, A., Sánchez, Á., Fernández, F., & Montemayor, A. S. (2006), “Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic,” Pattern Recognition Letters, 27(11), 1239-1251.
• Rojas, R. (1996), “The backpropagation algorithm,” In Neural Networks (pp. 149- 182).Springer Berlin Heidelberg.