Exudates Detection in Fundus Images
DOI:
https://doi.org/10.26438/ijcse/v7i6.976980Keywords:
diabetic retinopathy, sensitivity, specificity, accuracy, exudatesAbstract
Diabetic retinopathy is the main cause of vision loss in diabetic patients. It is caused by the damage of retinal blood vessels due to prolonged diabetes. This paper investigates on some image processing operations to extract exudates for the analysis of diabetic retinopathy. The proposed method stands out prominent in terms of specificity and accuracy.
References
[1] K. Ram and J. Sivaswamy, “Multi-space clustering for segmentation of exudates in retinal color photographs,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1437–1440, Minneapolis, Minn, USA, September 2009.
[2] I. Soares, M. Castelo-Branco, and A. M. G. Pinheiro, “Exudates dynamic detection in retinal fundus images based on the noise map distribution,” in Proceedings of the 19th IEEE European Signal Processing Conference (EUSIPCO ’11), pp. 46–50, Barcelona, Spain, September 2011.
[3] C. Jayakumari and T. Santhanam, “An intelligent approach to detect hard and soft exudates using echo state neural network,” Information Technology Journal, vol. 7, no. 2, pp. 386–395, 2008.
[4] D. Kayal and S. Banerjee, “A new dynamic thresholding based technique for detection of hard exudates in digital retinal fundus image,” in Proceedings of the 1st International Conference on Signal Processing and Integrated Networks (SPIN ’14), pp. 141–144, February 2014.
[5] F. Amel, M. Mohammed, and B. Abdelhafid, “Improvement of the hard exudates detection method used for computer aided diagnosis of diabetic retinopathy,” International Journal of Image, Graphics and Signal Processing, vol. 4, no. 4, pp. 19–27, 2012.
[6] P. M. Rokade and R. R. Manza, “Automatic detection of hard exudates in retinal images using haar wavelet transform,” Eye, vol. 4, no. 5, pp. 402–410, 2015.
[7] T. Jaya, J. Dheeba, and N. A. Singh, “Detection of hard exudates in colour fundus images using fuzzy support vector machine based expert system,” Journal of Digital Imaging, vol. 28, no. 6, pp. 761–768, 2015.
[8] A. Z. Rozlan, H. Hashim, S. F. Syed Adnan, C. A. Hong, and M. Mahyudin, “A proposed diabetic retinopathy classification algorithm with statistical inference of exudates detection,” in Proceedings of the International Conference on Electrical, Electronics and System Engineering (ICEESE ’13), pp. 90–95, IEEE, Kuala Lumpur, Malaysia, December 2013.
[9] K. Soman and D.Ravi, “Detection of exudates in human fundus image with a comparative study on methods for the optic disk detection,” in Proceedings of the IEEE International Conference on Information Communication and Embedded Systems (ICICES 14), pp. 1–5, Chennai, India, February 2014.
[10] R. Annunziata, A. Garzelli, L. Ballerini, A. Mecocci, and E. Trucco, “Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation,”IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 4, pp. 1129–1138, 2016.
[11] M. J. J. P. Van Grinsven, A. Chakravarty, J. Sivaswamy, T. Theelen, B. Van Ginneken, and C. I. Sanchez, “A bag of words approach for discriminating between retinal images containing exudates or drusen,” in Proceedings of the IEEE 10th International Symposium on Biomedical Imaging: from Nano to Macro (ISBI ’13), pp. 1444–1447, San Francisco, Calif, USA, April 2013.
[12] S.K. Badugu , R.K. Kontham, V.K. Vakulabharanam , B. Prajna, “Calculation of Texture Features for Polluted Leaves,” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.11-21, Feb (2018).
[13] A. Samant , S. Kadge, “Classification of a Retinal Disease based on Different Supervised Learning Techniques,” International Journal of Scientific Research in Network Security and Communication, Volume-5, Issue-3, June 2017.
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