Exudates Detection in Diabetic Retinopathy by Two Different Image Processing Techniques

Year 2018, , 177 - 186, 03.08.2018

Sara S. Aldeeb Selçuk Sevgen

https://doi.org/10.5152/iujeee.2018.1822

Abstract

DOI: 10.5152/iujeee.2018.1822

Different techniques developed in the
previous decades are used for blood vessel detection. Different kinds of image
processing approaches in the detection and analysis of blood vessels can be
applied to diagnose many human diseases and help in various medical and health
diagnoses. Image processing for blood vessels could be used in areas such as
disease diagnosis, severity measurement of specific diseases, and in biometric
security.

This study compares two different
techniques to accurately diagnose a specific disease according to some
selective features. Diabetic retinopathy is used for this comparative study as
it is one of the most severe eye disorders and chronic diseases to cause
blindness. Classifications and accurate measurements for blood vessel
abnormalities (exudates, hemorrhages, and micro-aneurysms) enabled the correct
and accurate diagnosis in retina and diabetic retinopathy. To avoid blindness,
it is essential to utilize fundus image processing application to facilitate
the early discovery of a diseased retinal. Throughout the fundus automated
image process, the retinal features are extracted. The techniques applied in
this study are a morphological-based image processing technique and an edge
detection technique using Kirsch’s template. First, the application of these
image processing techniques are described and explained in detail.
Subsequently, a classification process is proposed to assess and evaluate the
performance of each technique.

Keywords

Diabetic retinopathy, optic disc, exudates, morphology, Kirsch’s template

Exudates Detection in Diabetic Retinopathy by Two Different Image Processing Techniques

Abstract

DOI: 10.5152/iujeee.2018.1822

Different techniques developed in the previous decades are used for blood vessel detection. Different kinds of image processing approaches in the detection and analysis of blood vessels can be applied to diagnose many human diseases and help in various medical and health diagnoses. Image processing for blood vessels could be used in areas such as disease diagnosis, severity measurement of specific diseases, and in biometric security.

This study compares two different techniques to accurately diagnose a specific disease according to some selective features. Diabetic retinopathy is used for this comparative study as it is one of the most severe eye disorders and chronic diseases to cause blindness. Classifications and accurate measurements for blood vessel abnormalities (exudates, hemorrhages, and micro-aneurysms) enabled the correct and accurate diagnosis in retina and diabetic retinopathy. To avoid blindness, it is essential to utilize fundus image processing application to facilitate the early discovery of a diseased retinal. Throughout the fundus automated image process, the retinal features are extracted. The techniques applied in this study are a morphological-based image processing technique and an edge detection technique using Kirsch’s template. First, the application of these image processing techniques are described and explained in detail. Subsequently, a classification process is proposed to assess and evaluate the performance of each technique.

Keywords

Diabetic retinopathy, optic disc, exudates, morphology, Kirsch’s template

References

• 1. T. Chanwimaluang, G. Fan, "An efficient blood vessel detection algorithm for retinal images using local entropy thresholding in Circuits and Systems”, ISCAS'03. Proceedings of the 2003 International Symposium on IEEE, 2003. 2. M. Humayun, H. Lewis, H. W. Flynn, P. Sternberg, M. S. Blumenkranz, "Management of submacular hemorrhage associated with retinal arterial macroaneurysms", Am J Ophthalmol, vol. 126, no. 3, pp. 358-36, 1998. 3. J. Kanski, "Diabetic retinopathy, clinical ophthalmology", Oxford: Butterworth-Heimann, 1997. 4. P. G. Swann, "Non‐retinal ocular changes in diabetes", Clin Exp Optom, vol. 82, no. 2‐3, pp. 43-46, 1999. 5. M. Niemeijer, B. Van Ginneken, Staal, J. Stall, M. S. Suttorp-Schulten, M. D. Abràmoff, "Automatic detection of red lesions in digital color fundus photographs." IEEE Trans on Med imaging, vol. 24, no. 5, pp. 584-592, 2005. 6. C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal, and D. Usher, "Automated detection of diabetic retinopathy on digital fundus images", Diabet Med, vol. 19, no. 2, pp.105-112, 2002. 7. D. Usher, M. Dumskyj, M. Himaga, TH. Williamson, S. Nussey, J. Boyce. "Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening", Diabet Med, vol. 21, no. 1, pp.84-90, 2004. 8. G.G. Gardner, D. Keating, T.H. Williamson, A.T. Elliott, "Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool", Br J Ophthalmol, vol. 80, no. 11, pp.940-944, 1996. 9. Z. Liu, C. Opas, and S.M. Krishnan, "Automatic image analysis of fundus photograph.", In Engineering in Medicine and Biology Society, Proceedings of the 19th Annual International Conference of the IEEE 1997, pp. 524-525. 10. A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, "Automated identification of diabetic retinal exudates in digital colour images", Br J Ophthalmol, vol. 87, no. 10, pp.1220-1223, 2003. 11. S.K. Mitra, T.W. Lee, and M. Goldbaum, "A Bayesian network based sequential inference for diagnosis of diseases from retinal images.", Pattern Recognition Lett, vol. 26, no. 4, pp.459-470, 2005. 12. T. Kauppi, V. Kalesnykiene, J.K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Kälviäinen, and J. Pietilä, "DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms", Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, 73, 2006. 13. STARE Project Website. Clemson, SC, Clemson Univ. [Online]. Available: http://www.ces.clemson.edu/~ahoover/stare/ 14. R.C. Gonzales, R.E. Woods, "Digital image processing", New York: Addison- Wesley; 1993. p. 75-140. 15. A. Sopharak, B. Uyyanonvara, S. Barman, T.H. Williamson, "Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods", Comput Med Imaging Graph, vol. 32, no. 8, pp.720-727, 2008. 16. T. Walter, J.C. Klein, P. Massin, A. Erginay, "A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina.", IEEE Trans Med Imaging, vol. 21, no. 10, pp.1236-1243, 2002. 17. C.I. Sánchez, R. Hornero, M.I. Lopez, and J. Poza, "Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy", Engineering in Medicine and Biology Society, IEMBS'04. 26th Annual International Conference of the IEEE, 2004, pp. 1624-1627. 18. Z. Xiaohui, and A. Chutatape, "Detection and classification of bright lesions in color fundus images", In Image Processing, 2004. ICIP'04. 2004 International Conference on IEEE, 2004, pp. 139-142. 19. A.K. Jain, "Fundamentals of digital image processing", Prentice-Hall, Inc., 1989. 20. S. Badsha, A.W. Reza, K.G. Tan, K. Dimyati, "A new blood vessel extraction technique using edge enhancement and object classification.", J Digit Imaging, vol. 26, no. 6, pp.1107-1115, 2013. 21. R. Akhavan, and K. Faez, , "A novel retinal blood vessel segmentation algorithm using fuzzy segmentation", IJECE, vol. 4, vol. 4, pp.561-572, 2014. 22. M. García, C.I. Sánchez, M.I. López, D. Abásolo, R. Hornero, "Neural network-based detection of hard exudates in retinal images.", Comput Methods Programs Biomed, vol. 93, no. 1, pp.9-19, 2009.