Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Silfia Andini, Anjar Wanto, Retno Devita, Ruri Hartika Zain, Aulia Fitrul Hadi


Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset—data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.


Edge detection; hybrid; canny; Zerocross; Retina.

Full Text:



D. Chen et al., “Deep learning and alternative learning strategies for retrospective real-world clinical data,†npj Digital Medicine, vol. 2, no. 1, pp. 1–5, 2019.

M. Haenlein, E. Anadol, T. Farnsworth, H. Hugo, J. Hunichen, and D. Welte, “Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, & Co.,†California Management Review, vol. 63, no. 1, pp. 5–25, 2020.

S. Rahmawati, R. Devita, R. H. Zain, E. Rianti, N. Lubis, and A. Wanto, “Prewitt and Canny Methods on Inversion Image Edge Detection: An Evaluation,†Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012039, 2021.

Y.-J. Zhang, “Development of Image Engineering in the Last 20 Years,†Encyclopedia of Information Science and Technology, Fourth Edition, pp. 1319–1330, 2018.

Y.-J. Zhang, Image Engineering. In: Handbook of Image Engineering. Springer, Singapore, 2021.

Y.-J. Zhang, “Image Engineering,†in Handbook of Image Engineering, Springer, Singapore, 2021, pp. 55–83.

A. Distante and C. Distante, Handbook of Image Processing and Computer Vision, vol. 1. 2020.

Y. Xu et al., “Artificial intelligence: A powerful paradigm for scientific research,†The Innovation, vol. 2, no. 4, pp. 1–20, 2021.

F. Zangeneh-Nejad, D. L. Sounas, A. Alù, and R. Fleury, “Analogue computing with metamaterials,†Nature Reviews Materials, vol. 6, no. 3, pp. 207–225, 2021.

L. H. Gong, C. Tian, W. P. Zou, and N. R. Zhou, “Robust and imperceptible watermarking scheme based on Canny edge detection and SVD in the contourlet domain,†Multimedia Tools and Applications, vol. 80, no. 1, pp. 439–461, 2021.

R. Ranjbarzadeh, S. B. Saadi, and A. Amirabadi, “LNPSS: SAR image despeckling based on local and non-local features using patch shape selection and edges linking,†Measurement: Journal of the International Measurement Confederation, vol. 164, p. 107989, 2020.

S. Aouat, I. Ait-hammi, and I. Hamouchene, “A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix,†Multimedia Tools and Applications, vol. 80, no. 16, pp. 24027–24052, 2021.

M. Mittal et al., “An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis,†IEEE Access, vol. 7, pp. 33240–33255, 2019.

J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, “Image Matching from Handcrafted to Deep Features: A Survey,†International Journal of Computer Vision, vol. 129, no. 1, pp. 23–79, 2021.

Z. Wei and G. H. Liu, “Image retrieval using the intensity variation descriptor,†Mathematical Problems in Engineering, vol. 2020, pp. 1–12, 2020.

M. Gholizadeh-Ansari, J. Alirezaie, and P. Babyn, “Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer,†Journal of Digital Imaging, vol. 33, pp. 504–515, 2020.

S. Sengupta, N. Mittal, and M. Modi, “Improved skin lesion edge detection method using Ant Colony Optimization,†Skin Research & Technology, vol. 25, no. 6, pp. 846–856, 2019.

C. J. J. Sheela and G. Suganthi, “Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm,†Multimedia Tools and Applications, vol. 79, no. 25–26, pp. 17483–17496, 2020.

R. G. Zhou, H. Yu, Y. Cheng, and F. X. Li, “Quantum image edge extraction based on improved Prewitt operator,†Quantum Information Processing, vol. 18, no. 261, pp. 1–24, 2019.

P. Jayapriya and S. Hemalatha, “Detection of Maize Stem and Leaf Diseases using Edge Detection Method to Prevent the Crops from Diseases,†Journal of Xi’an University of Architecture & Technology, vol. 12, no. 7, pp. 1052–1064, 2020.

M. Gandhi, J. Kamdar, and M. Shah, “Preprocessing of Non-symmetrical Images for Edge Detection,†Augmented Human Research, vol. 5, no. 1, pp. 1–10, 2020.

V. R. Balaji, S. T. Suganthi, R. Rajadevi, V. Krishna Kumar, B. Saravana Balaji, and S. Pandiyan, “Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier,†Measurement: Journal of the International Measurement Confederation, vol. 163, p. 107922, 2020.

B. Watkins and A. van Niekerk, “A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery,†Computers and Electronics in Agriculture, vol. 158, no. November 2018, pp. 294–302, 2019.

M. Versaci and F. C. Morabito, “Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence,†International Journal of Fuzzy Systems, vol. 23, pp. 918–936, 2021.

Y. Liu, M.-M. Cheng, D.-P. Fan, L. Zhang, J.-W. Bian, and D. Tao, “Semantic Edge Detection with Diverse Deep Supervision,†International Journal of Computer Vision, vol. 130, pp. 179–198, 2022.

G. Chen, Z. Jiang, and M. M. Kamruzzaman, “Radar remote sensing image retrieval algorithm based on improved Sobel operator,†Journal of Visual Communication and Image Representation, vol. 71, no. 102720, pp. 1–8, 2020.

M. Yasir et al., “Automatic Coastline Extraction and Changes Analysis Using Remote Sensing and GIS Technology,†IEEE Access, vol. 8, pp. 180156–180170, 2020.

Erwin and T. Yuningsih, “Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection,†Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 9, pp. 1–12, 2020.

B. Iqbal, W. Iqbal, N. Khan, A. Mahmood, and A. Erradi, “Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark,†Cluster Computing, vol. 23, no. 1, pp. 397–408, 2020.

Y. Cho et al., “Keypoint Detection Using Higher Order Laplacian of Gaussian,†IEEE Access, vol. 8, pp. 10416–10425, 2020.

A. Wanto, S. D. Rizki, S. Andini, S. Surmayanti, N. L. W. S. R. Ginantra, and H. Aspan, “Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification,†Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012037, 2021.

Z. Selmi, M. Ben Halima, U. Pal, and M. A. Alimi, “DELP-DAR system for license plate detection and recognition,†Pattern Recognition Letters, vol. 129, pp. 213–223, 2020.

X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,†Neurocomputing, vol. 396, pp. 39–64, 2020.

W. Cao, Q. Liu, and Z. He, “Review of Pavement Defect Detection Methods,†IEEE Access, vol. 8, pp. 14531–14544, 2020.

X. Ye and Q. Wang, “Active Contour Image Segmentation Method for Training Talents 0f Computer Graphics and Image Processing Technology,†IEEE Access, pp. 1–1, 2020.

S. Deenan, S. Janakiraman, and S. Nagachandrabose, “Image Segmentation Algorithms for Banana Leaf Disease Diagnosis,†Journal of The Institution of Engineers (India): Series C, vol. 101, no. 5, pp. 807–820, 2020.

J. Shi, H. Jin, and Z. Xiao, “A novel hybrid edge detection method for polarimetric SAR images,†IEEE Access, vol. 8, pp. 8974–8991, 2020.

M. A. Kats, “Dark field on a chip,†Nature Photonics, vol. 14, no. 5, pp. 266–267, 2020.

P. Dubey, P. K. Dubey, and S. Changlani, “A Hybrid Technique for Digital Image Edge Detection by Combining Second Order Derivative Techniques Log and Canny,†IEEE Xplore, pp. 1–6, 2020.

S. Sumijan, S. Arlis, and P. A. W. Purnama, “Fingerprint Identification Using the Hybrid Thresholding and Edge detection for the Room Security,†TEM Journal, vol. 9, no. 4, pp. 1396–1400, 2020.

“Structured Analysis of the Retina.†[Online]. Available: https://cecas.clemson.edu/~ahoover/stare/. [Accessed: 02-Mar-2021].

DOI: http://dx.doi.org/10.18517/ijaseit.12.4.17229


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development