Extracting Features from the fundus image using Canny edge detection method for PreDetection of Diabetic Retinopathy

EOI: 10.11242/viva-tech.01.04.001

Download Full Text here


Ms. Nishant Dandekar, Ms. Jayesh Kulkarni, Ms. Riddhi Raut, Karishma Raut, "Extracting Features from the fundus image using Canny edge detection method for PreDetection of Diabetic Retinopathy ", VIVA-IJRI Volume 1, Issue 4, Article 52, pp. 1-6, 2021. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Diabetic Retinopathy (DR) is an ailment of the eye caused by diabetes. People suffering from diabetes can procure the disease. DR is caused when the high blood sugar level damages the blood vessels of the eye. Also due to high blood sugar level abnormal blood vessels can grow in the retina. This can make the patient lose its vision. Unfortunately, the symptoms of DR cannot be detected easily. The disease can grow increasingly if left untreated. Hence it becomes all the more important to detect DR. In this paper we have made use of Image processing Technique like canny edge detection to extract the features necessary to detect DR and find the severity of the disease. The features extracted from the image can be used to detect DR by various other methods like SVM, Logistic Regression, etc.


Diabetic Retinopathy, Feature Extraction, Canny Edge Detection, Image Processing Technique


  1. Subhashini, R., Nithin, T. N. R., & Koushik, U. M. S. Diabetic Retinopathy Detection using Image Processing (GUI).
  2. Bhatia, K., Arora, S., & Tomar, R. (2016, October). Diagnosis of diabetic retinopathy using machine learning classification algorithm. In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (pp. 347-351). IEEE.
  3. Conde, P. P., De la Calleja, J., Benitez, A., & Medina, M. A. (2012, November). Image-based classification of diabetic retinopathy using machine learning. In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 826-830). IEEE.
  4. Kumaran, Y., & Patil, C. M. (2018). A Brief Review of the Detection of Diabetic Retinopathy in Human Eyes Using Pre-Processing & Segmentation Techniques. International Journal of Recent Technology and Engineering, 7(4), 310-320.
  5. Chang, Samuel H., et al. "Small retinal vessel extraction using modified Canny edge detection." 2008 International Conference on Audio, Language and Image Processing. IEEE, 2008.
  6. Dhar, Rajdeep, Radheshyam Gupta, and K. L. Baishnab. "An analysis of CANNY and LAPLACIAN of GAUSSIAN image filters in regard to evaluating retinal image." 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). IEEE, 2014.
  7. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698.
  8. Aquino, A., Gegúndez-Arias, M. E., & Marín, D. (2010). Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE transactions on medical imaging, 29(11), 1860-1869.
  9. K. T.Ilayarajaa and E. Logashanmugam, "RetinalBlood Vessel Segmentation usingMorphological andCanny Edge Detection Technique," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020, pp. 1-5, doi:10.1109/ICSCAN49426.2020.9262446.
  10. Mendonca, A. M., & Campilho, A. (2006). Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE transactions on medical imaging, 25(9), 1200-1213.
  11. Gharaibeh, N., Al-Hazaimeh, O. M., Al-Naami, B., & Nahar, K. M. (2018). An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. International Journal ofSignal and Imaging Systems Engineering, 11(4), 206-216.
  12. Xu, Z., Baojie, X., & Guoxin, W. (2017, October). Canny edge detection based on Open CV. In 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI) (pp. 53-56). IEEE.
  13. R., Rasika & A., Swati & D., Gautami & B., Mayuri & Vaidya, Archana. (2016). Quality Control of PCB using Image Processing. International Journal of Computer Applications. 141. 28-32. 10.5120/ijca2016909623.
  14. https://docs.opencv.org/3.4/d7/de1/tutorial_js_canny.htm