Retinal Vessel Image Segmentation Based on Guided Filter and Neural Networks

Authors

• Ibrahem Al-Jadir, Ministry of Higher Education and Scientific Research, Baghdad, Iraq
• Jawad S. Hameed, Ministry of Higher Education and Scientific Research, Baghdad, Iraq
• Husam Yahya, Ministry of Higher Education and Scientific Research, Baghdad, IraqFollow
• Dalia N. Al-Oboudi, Department of oral and maxillofacial surgery, College of Dentisty, University of Baghdad, Iraq

Abstract

Accurate segmentation of retinal blood vessels is essential for the early diagnosis and monitoring of various ophthalmic and systemic diseases. This work shows how to use a modified U-Net architecture to improve how retinal blood vessels are segmented. The method uses rolling guidance filters with two different sets of parameters to make the edges of the vessels sharper and make it easier to tell them apart. Separate U-Net models process each filtered image independently, and then the outputs are combined to make the final segmentation result. Both networks are trained at the same time using a shared loss function. Tests on many benchmarks retinal image datasets show that the suggested method makes segmentation far more accurate than the usual U-Net model.

Recommended Citation

Al-Jadir, Ibrahem; Hameed, Jawad S.; Yahya, Husam; and Al-Oboudi, Dalia N. (2025) "Retinal Vessel Image Segmentation Based on Guided Filter and Neural Networks," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 3: Iss. 1, Article 21.
DOI: https://doi.org/10.58496/MJAIH/2025/022

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.