Efficient Segmentation of Retinal Blood Vessels in ‎The Fundus Images Using Morphological Image Processing

  • Authors

    • M. Ramkumar Prabhu Department of Electronics and Communication Engineering,‎ Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,‎ Saveetha University, Chennai, Tamil Nadu, India
    • J. R. Arunkumar Department of Computer Science and Engineering ‎, Modern Institute of Technology and Research Centre ‎Alwar, Rajasthan, India
    • R. Anusuya Department of Computer Science and Engineering ‎, Modern Institute of Technology and Research Centre ‎Alwar, Rajasthan, India
    • G. Charulatha Department of Electronics and Communication Engineering,‎ Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,‎ Saveetha University, Chennai, Tamil Nadu, India
    • Navaprakash. N Department of Electronics and Communication Engineering,‎ Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,‎ Saveetha University, Chennai, Tamil Nadu, India
    https://doi.org/10.14419/ryng1670

    Received date: July 31, 2025

    Accepted date: September 1, 2025

    Published date: October 12, 2025

  • Fundus Image; Blood Vessels; CLAHE; Segmentation; Otsu Thresholding

  • Abstract

    The segmentation of retinal blood vessels is critical in diagnosing and monitoring a variety of retinal ‎disorders, such as hypertension, diabetes, glaucoma, and other ocular and cardiovascular problems. ‎Retinal fundus pictures can aid in the early identification and management of these disorders by ‎providing essential details about the status of the blood vessels in the retina. Manual evaluation of retinal ‎fundus pictures by qualified ophthalmologists or experts takes time and requires competence. To address ‎these issues, an automated system for the purpose of diagnosing and treating eye illnesses, a ‎computerized image processing tool to divide the blood vessels that comprise of the ‎retina. The vessel extraction methodology is carried out in three stages: preprocessing, blood vessel ‎segmentation, and performance metric analysis. The green channel is extracted from the input image ‎during preprocessing, and the contrast is enhanced using CLAHE. After preprocessing, the image ‎undergoes a morphological operation followed by Otsu thresholding to segment the image has pixels that ‎represent vessels and non-vessels, respectively. This method is validated on the DRIVE and HRF ‎datasets, and the performance is measured in terms of accuracy, sensitivity, and specificity‎.

  • References

    1. M. F. Aslan, M. Ceylan, and A. Durdu, “Segmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machines,” 2018 Int. Conf. Artif. Intell. Data Process. IDAP 2018, no. September, pp. 1–5, 2019, https://doi.org/10.1109/IDAP.2018.8620890.
    2. U. Ozkava, S. Ozturk, B. Akdemir, and L. Sevfi, “An Efficient Retinal Blood Vessel Segmentation using Morphological Operations,” ISMSIT 2018 - 2nd Int. Symp. Multidiscip. Stud. Innov. Technol. Proc., 2018, https://doi.org/10.1109/ISMSIT.2018.8567239.
    3. Z. Li, M. Jia, X. Yang, and M. Xu, “Blood vessel segmentation of retinal image based on dense-U-net network,” Micromachines, vol. 12, no. 12, 2021, https://doi.org/10.3390/mi12121478.
    4. E. Tuba, L. Mrkela, and M. Tuba, “Retinal blood vessel segmentation by support vector machine classification,” 2017 27th Int. Conf. Radioelektronika, RADIOELEKTRONIKA 2017, pp. 1–6, 2017, https://doi.org/10.1109/RADIOELEK.2017.7936649.
    5. X. Deng and J. Ye, “A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network,” Biomed. Signal Process. Control, vol. 73, no. October 2021, p. 103467, 2022, https://doi.org/10.1016/j.bspc.2021.103467.
    6. O. Ramos-Soto et al., “An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering,” Comput. Methods Programs Biomed., vol. 201, 2021, https://doi.org/10.1016/j.cmpb.2021.105949.
    7. Y. Ma, Z. Zhu, Z. Dong, T. Shen, M. Sun, and W. Kong, “Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Fil-ter and U-Net Network,” Biomed Res. Int., vol. 2021, 2021, https://doi.org/10.1155/2021/5561125.
    8. Z. Huang, Y. Fang, H. Huang, X. Xu, J. Wang, and X. Lai, “Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach,” Sci. Program., vol. 2021, 2021, https://doi.org/10.1155/2021/5520407.
    9. Simon, J., Kapileswar, N. and Kumar, A.M., 2025. A Novel Energy Adaptive Neural Network and Deep Q-Learning Network for Improved Energy Efficiency in Dynamic Underwater IoT Environment. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3597025.
    10. S. Dash and M. R. Senapati, “Enhancing detection of retinal blood vessels by combined approach of DWT, Tyler Coye and Gamma correction,” Bio-med. Signal Process. Control, vol. 57, p. 101740, 2020, https://doi.org/10.1016/j.bspc.2019.101740.
    11. Upadhyay, Kamini, Monika Agrawal, and Praveen Vashist. "Unsupervised multiscale retinal blood vessel segmentation using fundus images." IET Im-age Processing 14, no. 11 (2020): 2616-2625. https://doi.org/10.1049/iet-ipr.2019.0969.
    12. Toptaş, Buket, and Davut Hanbay. "Retinal blood vessel segmentation using pixel-based feature vector." Biomedical Signal Processing and Control 70 (2021): 103053. https://doi.org/10.1016/j.bspc.2021.103053.
    13. Rakshit, J.K., Selvam, P., Selvakumarasamy K et. al., “Design of all-Optical Transmission Gate Using Silicon Microring Resonator”, Brazilian Journal of Physics, vol. 55, 94 (2025). https://doi.org/10.1007/s13538-025-01730-z.
    14. Sathananthavathi, Vallikutti, and G. Indumathi. "Encoder enhanced atrous (EEA) unet architecture for retinal blood vessel segmentation." Cognitive Systems Research 67 (2021): 84-95. https://doi.org/10.1016/j.cogsys.2021.01.003.
    15. Dash, Sonali, Manas Ranjan Senapati, Pradip Kumar Sahu, and P. S. R. Chowdary. "Illumination normalized based technique for retinal blood vessel segmentation." International Journal of Imaging Systems and Technology 31, no. 1 (2021): 351-363. https://doi.org/10.1002/ima.22461.
    16. Enireddy, V., Somasundaram, K., Prabhu, M.R. and Babu, D.V., 2021, October. Data obfuscation technique in cloud security. In 2021 2nd Interna-tional Conference on Smart Electronics and Communication (ICOSEC) (pp. 358-362). IEEE. https://doi.org/10.1109/ICOSEC51865.2021.9591915.
    17. Srikanth, G., Raghavendran, C.V., Prabhu, M.R., Radha, M., Kumari, N.S. and Francis, S.K., 2025. Climate Change Impact on Geographical Region and Healthcare Analysis Using Deep Learning Algorithms. Remote Sensing in Earth Systems Sciences, 8(1), pp.182-190. https://doi.org/10.1007/s41976-024-00187-z.
    18. Krishnamoorthy, V., Veeramani, T., Indirani, M., Sathishkumar, B. R., Sasi, G., & Selvakumarasamy, K. (2025). Autism Spectrum Disorder Prediction from Facial Images Using Fine-Tuned Efficient Net B0–B7 ‎Architectures. International Journal of Basic and Applied Sciences, 14(5), 379-389. https://doi.org/10.14419/5fkr4104.
    19. Ravikumar, C.V, 2025. Modelling and Design of a Hexagonal Grating Structure for Underwater Acoustic Wave Sensing. Results in Engineering, p.104148. https://doi.org/10.1016/j.rineng.2025.104148.
    20. Sathish, K., 2025, February. Investigation of Impedance Tube-based Parameter Estimation Techniques with Data Generation for Underwater Acoustic Applications. In 2025 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 154-159). IEEE. https://doi.org/10.1109/ICEARS64219.2025.10940315.

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  • How to Cite

    Prabhu , M. R. ., Arunkumar , J. R. ., Anusuya , R. ., Charulatha , G. ., & N, N. . (2025). Efficient Segmentation of Retinal Blood Vessels in ‎The Fundus Images Using Morphological Image Processing. International Journal of Basic and Applied Sciences, 14(6), 222-227. https://doi.org/10.14419/ryng1670