Vessel Segmentation for Fundus Images Based on High-Resolution Networks with Gamma Correction and CLAHE Preprocessing

Retinal vessel segmentation plays a crucial role in the early diagnosis and treatment of ophthalmic diseases, yet manual segmentation methods are time-consuming and prone to human error. The increasing demand for automated solutions has led to the exploration of deep learning-based approaches that improve segmentation accuracy and efficiency. However, existing methods often struggle with poor image resolution, a lack of robustness across datasets, and challenges in detecting fine vessel structures, limiting their applicability in large-scale clinical settings. To address these limitations, this study proposes a deep learning-based framework using high-resolution network (HRNet) combined with an advanced preprocessing pipeline, including grayscale conversion, gamma correction, CLAHE, and normalization, to enhance image quality for segmentation. The proposed HRNet-based framework was evaluated on two publicly available datasets, DRIVE and CHASE_DB1. It achieved the highest sensitivity of 0.8193 on DRIVE and the highest specificity of 0.9807 on CHASE_DB1, demonstrating its capability to balance fine vessel detection and false positive reduction. These findings indicate that the proposed framework effectively improves the segmentation accuracy of ophthalmoscope images, enhancing its potential for clinical decision-making in ophthalmology.