Automatic Diagnosis of Diabetic Retinopathy Using Morphological Operations

  • Sasuee Rajper Mehran University of Engineering and Technology Jamshoro, 76090, Pakistan
  • Ahsan Ahmed Ursani
  • Sehreen Moorat Liaquat University of Medical Health and Sciences Jamshoro, 76090, Pakistan
Keywords: Hemorrhage dtetction, SVM classifier, Morphological Operations


Diabetic retinopathy is diabetic eye disease or a sight threatening complication (one of the major cause of blindness) for the person suffering from diabetes which causes progressive loss to the retina, in which retina of the eye is affected because the capillaries of the retina are damaged. Diabetic Retinopathy is unpredictable at early stage, it is only predictable in advanced stage when diabetic patient suffers from loss of vision due to leakage of lipid, blood vessels bursts and there is formation of new fragile blood vessels which blocks the blood supply to retina. Diabetic Retinopathy include Microaneurysm, hemorrhage and exudates. However, early detection and treatment is most important that can reduce the chances of occurrences of blindness about 95%. To analyze Microaneurysm and hemorrhage as early stages of DR is a challenging task for Ophthalmologists to prevent vision loss. Automatic analysis of Diabetic Retinopathy helps in preventing vision loss. Our proposed method is based on automatic detection of hemorrhage using colorful fundus images. In proposed work we have used supervised learning to classify the data as hemorrhage and without hemorrhage with SVM classifier. To find hemorrhage and its severity, we have extracted statistical features (including standard deviation, energy, entropy and contrast of an image), used classification approach and then segmentation methods. After feature detection, Morphological Operations are applied to detect blood vessels and hemorrhage detection with help of segmentation technique. Here the threshold optimization, Grey Wolf Optimization (GWO) techniques are used in our proposed work for getting maximum accuracy, sensitivity and specificity performance metrics.


. Jalan, S., & Tayade, A. A. (2015). Review paper on Diagnosis of Diabetic Retinopathy using KNN and SVM Algorithms. International Journal of Advance Research in Computer Science and Management Studies, 3(1), 128-131.

. K. Adem, M. Hekim, and S. Demir, “Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms,” Turkish Journal Of Electrical Engineering & Computer Sciences, vol. 27, no. 1, pp. 499–515, 2019.

. R. Mumtaz, M. Hussain, S. Sarwar, K. Khan, S. Mumtaz, and M. Mumtaz, “Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients,” International Journal of Diabetes in Developing Countries, vol. 38, no. 1, pp. 80–87, Feb. 2017.

. S. Lahmiri and A. Shmuel, “Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages,” Optics & Laser Technology, vol. 96, pp. 243–248, 2017.

. G. A. L and K. Parasuraman, “Detection of retinal hemorrhage from fundus images using ANFIS classifier and MRG segmentation,” Biomedical Research, vol. 29, no. 7, 2018

. V. Satyananda, K. V Narayanaswamy, and K. Karibasappa, “Extraction of Exudates from the Fundus Images A Review,” vol. 5, no. 12, pp. 133–139, 2016.

. M. A. Fkirin, S. Badawy, and A. El, “Early Detection of Diabetic Retinopathy in Fundus Images Using Image Filtration,” vol. 5, no. 1, pp. 560–564, 2014.

. A. Pattanashetty, “Diabetic Retinopathy Detection using Image Processing : A Survey,” vol. 5, no. 4, pp. 661–666, 2016.

. T. Ahmed, S. Junbin, G. Tariq, K. Ahmad, and F. M. Hani, “Computerised approaches for the detection of diabetic retinopathy using retinal fundus images : a survey,” Pattern Anal. Appl., 2017.

. K. H. Babu, “Blood Vessel Segmentation in Angiograms using Fuzzy Inference System and Mathematical Morphology,” vol. 2, no. 1, pp. 72–77, 2015.

. N. B. Prakash, D. Selvathi, and G. R. Hemalakshmi, “Development of Algorithm for Dual Stage Classification to Estimate Severity Level of Diabetic Retinopathy in Retinal Images using Soft Computing Techniques,” vol. 6, no. 4, pp. 717–739, 2014.

. [“UNSUPERVISED CURVATURE-BASED RETINAL VESSEL SEGMENTATION Saurabh Garg , Jayanthi Sivaswamy , Siva Chandra,” pp. 344–347, 2007.

. D. Gowrishankar, G. Ramprabu, E. Bharathi, R. Divya, and S. Thulasi, “Automatic Extraction of Blood Vessels and Exudates Segmentation for Diabetic Retinopathy Detection,” pp. 1413–1417, 2015.

. Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2016). Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain informatics, 3(4), 249-267.

. Jitpakdee P, Aimmanee P, Uyyanonvara B. A survey on hemorrhage detection in diabetic retinopathy retinal images. 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Phetchaburi, 2012.

. Acharya UR, Lim CM, Ng EYK, Chee C, Tamura T. Computerbased detection of diabetes retinopathy stages using digital fundus images in Proceedings of the Institution of Mechanical Engineers. Journal of Engineering in Medicine: 545–553. 2009

. Devaraj D. Detection of red lesion in diabetic retinopathy using adaptive thresholding method. International Journal of Engineering Research and Technology. 2013;2(4):1889–1892.