Designing of Computer Aided Diagnostic System for the Identification of Exudates in Retinal Fundus Images


  • santosh kumar mishra Thapar university
  • Deepti Mittal Electrical and Instrumentation Engineering Department, Thapar University, Patiala (Punjab), India
  • Ramesh kumar sunkaria Department of electronic and communication, Dr.B.R. Ambedkar NIT Jalandher , jalandher, India



Macular edema, Diabetic retinopathy, Exudate, Retina fundus images, Morphological reconstruction, Normalization, Candidate exudates detection.


Macular edema is an advance stage of diabetic retinopathy which affects central vision of diabetes patients. The main cause of edema is the appearance of exudates near or on macular region in human retina. If the exudates are present in the macular region of retina, it will lead to diabetic macular edema. Early detection of macular edema in diabetic patients paves a path for prevention from blindness. The automatic system for early detection of diabetic macular edema should identify all possible exudates present on the surface of retina. In the proposed work, a computer added diagnosis system is design for the identification of the exudates in color retinal fundus images. The system consists of three stages; candidate exudates detection, feature extraction and classification. The system is designed with (i) background estimation, morphological reconstruction, normalization for candidate exudates detection (ii) Gray level co-occurrence matrix for feature extraction and (iii) support vector machine for classification. The classifier classifies in between the region of exudates and non-exudates. The system performance is evaluated in terms of the parameters such as sensitivity, specificity, mathews correlation coefficient, positive predicative value, and accuracy whose values are 88.23%, 100%, 88.23%, 100%, 93.75% respectively.

Author Biography

santosh kumar mishra, Thapar university

electrical and instrumentataion enginering department,


(1) D. E. Singer, D. M. Nathan, H. A. Fogel, and A. P. Schachat, “Screening for diabetic retinopathy.” Ann Intern Med, vol. 116, no. 8, 1992. P: 660–671.

(2) M. D. Abramoff, M. Niemeijer, M. S. A. Suttorp-Schulten, M. A. Viergever, S. R. Russell, and B. van Ginneken, “Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes.” Diabetes Care, vol. 31, no. 2, 2008. P: 193–198.

(3) S. Philip, A. D. Fleming, K. A. Goatman, S. Fonseca, P. McNamee, G. S. Scotland, G. J. Prescott, P. F. Sharp, and J. A. Olson, “The efficacy of automated ”disease/no disease” grading for diabetic retinopathy in a systematic screening programme.” Br J Ophthalmol, vol. 91, no. 11, 2007.P:1512–1517.

(4) M.D. Abramoff, M.K. Garvin, Sonka, M., ”Retinal Imaging and Image Analysis,” IEEE reviews in Biomedical Engineering, vol.3, 2010. P:169.

(5) M. U. Akram and S. A. Khan, ”Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy”, Journal of Medical System, vol.36, no.5, 2011.P:3151-3162.

(6) A. Tariq, M. U. Akram, A. Shaukat, S. A. Khan, “Automated Detection and Grading of Diabetic Maculopathy in Digital Retinal Images”, Journal of Digital Imaging, vol. 26, no. 4, 2013. P: 803-812, 2013.

(7) M. U. Akram, A. Tariq, M. A. Anjum, M. Y. Javed, ”Automated Detection of Exudates in Colored Retinal Images for Diagnosis of Diabetic Retinopathy”, OSA Journal of Applied Optics, vol. 51 no. 20,2012.P: 4858-4866.

(8) R. Phillips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates, ” Graefes Arch Clin Exp Ophthalmol, vol. 231, no. 2,1993. P: 90–94.

(9) 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,” Diabetic Medicine, vol. 19, no. 2,2002.P: 105–112.

(10) H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 2, 2004. P: 246–254.

(11) T. Walter, J. K. Klein, P. Massin, and 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 Transactions

on Medical Imaging, vol. 21, no. 10, 2002.P: 1236–1243.

(12) A. Sopharak, B. Uyyanonvara, S. Barman, and T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods.” Computerized Medical Imaging and Graphics, vol. 32, no. 8, 2008.P: 720–727.

(13) D. Mittal, V. kumar, S. C. Saxena, N.Khandelwal, ”Neural networks based focal liver diagnosis using ultrasound images,”Computerized Medical Imaging and Graphics, vol.35,no-4,2011.P:135-323.

(14) K. Kumari, D. Mittal, ”Automated Drusen Detection Technique for Age-Related Macular Degeneration” Society for science and education, united kingdom,vol.2,2015,P:19-26.

(15) R. Phillips, T. Spencer, P. Ross, P. Sharp, and J. Forrester, “Quantification of diabetic maculopathy by digital imaging of the fundus,” Eye, vol. 5, 1991.P: 130–137.

(16) R. Phillips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates,” Graefe’s Arch. Clin. Exp. Ophthalmol., vol. 231, 1993.P: 90–94, 1993.

(17) B. Ege, O. Larsen, and O. Hejlesen, “Detection of abnormalities in retinal images using digital image analysis,” in Proc. 11th Scand. Conf. Image Process., vol. 13,2009.P: 833–840.

(18) H. Wang, H. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in retinal images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., Hilton Head Island, vol. 2, 2000. P: 181–187.

(19) M. Niemeijer, B. V. Ginneken, S. R. Russell, M. Suttorp, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Invest. Ophthalmol. Vis. Sci., vol. 48, 2007. P: 2260–2267.

(20) M. Goldbaum, S. Moezzi, A. Taylor, and S. Chatterjee, “Automated diagnosis and image understanding with object extraction, object classification and inferencing in retinal images,” in Proc. IEEE Int. Conf. Image Process., Lausanne, Switzerland, vol. 3,1996.P: 695–698.

(21) Alireza O., B. Shadgar and R. Markham, “A Computational-Intelligence- Based Approach for Detection of Exudates in Diabetic Retinopathy Images”, IEEE Trans on Information Tech in Biomedicine, vol. 13, No.4, 2009. P: 535-545.

(22) Ahmed Wasif Reza, C. Eswaran, Subhas Hati, “Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds”, Journal of Medical Systems, vol. 33,2009.P: 7380.

(23) S. Chugh, J Kaur,and D. mittal,”Exudates segmentation in retinal fundus image for detection of Diabetic retinopathy”, IJERT,vol.3,2014. P:673-677.

(24) Methods to evaluate segmentation and indexing techniques in the field of retinal ophtalmology. [Online]. Available:

(25) L. Giancardo, M. Abramoff, E. Chaum, T. Karnowski, F. Meriaudeau, and K. Tobin, “Elliptical local vessel density: a fast and robust quality metric for retinal images,” in Conf. of the IEEE EMBS, 2008.

(26) L. Giancardo,” Quality Assessment of Retinal Fundus Images using ELVD”. IN-TECH, 2010, ch. New Developments in Biomedical Engineering, 2010.P: 201–224.

(27) D. Mittal, V.Kumar, S.C.saxena, N. khandewal and N. kalra’’ Enhancement of the ultrasound images by modified anisotropic diffusion method”.Med. Biol.Engg. computer., vol.48, no-12,2010.P:1281-1291

(28) L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE Journal of Image Processing, vol. 2, no. 2, 1993.P: 176–201.

(29) A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp,“Automated microaneurysm detection using local contrast normalization and local vessel detection,” IEEE Transactions on Medical Imaging, vol. 25, no. 9,2006.P: 1223–1232..

(30) C. I. Sanchez, M. Garca, A. Mayo, M. I. Lopez, and R. Hornero, “Retinal image analysis based on mixture models to detect hard exudates.” Medical Image Analysis, vol. 13, no. 4, 2009. P: 650–658.

(31) M. Niemeijer, B. van Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abramoff, “Automatic detection of red lesions in digital color fundus photographs,” IEEE Trans Med Imag, vol. 24, no. 5, 2005.P: 584–

(32) J. Kaur, D. Mittal ” Segmentation and Measurement of Exudates in Fundus Images of the Retina for Detection of Retinal Disease.”Society for science and education, united kingdom, vol.2.2015.P:28-38.

(33) R. A. Kirsch, “Computer determination of the constituent structure of biological images.” Computers and Biomedical Research, vol. 4, no. 3,1971.P: 315–328.

(34) K. Shanmugam, R. M. Haralick and I. H. Dinstein, “Textural features for image classification” IEEE Transactions on Systems, Man and Cybernetics 3, 1973.P: 610 – 621.

(35) R. Kohavi and F. Provost. Glossary of terms, Special Issue on “Applications, of Machine Learning and the Knowledge Discovery Process”, Journal of Machine Learning, vol. 30, 1998. P: 271–274.




How to Cite

mishra, santosh kumar, Mittal, D., & sunkaria, R. kumar. (2015). Designing of Computer Aided Diagnostic System for the Identification of Exudates in Retinal Fundus Images. British Journal of Healthcare and Medical Research, 2(3), 29.

Most read articles by the same author(s)