Retinal Vessel Segmentation via Morphological Refinement and Adaptive Late Fusion

Abstract

Retinal vessel segmentation is fundamental for quantitative retinal vascular analysis; however, accurate delineation remains challenging because of nonuniform illumination, low-contrast capillaries, pathological lesions, and variations in image acquisition. This study presents a rule-based unsupervised retinal vessel segmentation framework based on decision-level adaptive late fusion, in which six complementary local adaptive thresholding methods are integrated and subsequently refined through luminance validation, hysteresis reconstruction, component cleanup, elongation filtering, and boundary smoothing. In this context, unsupervised denotes that no statistical segmentation model is trained using manual vessel annotations; instead, fixed parameters and fusion weights are determined from a small development subset, while all evaluation images remain unseen during method development. The proposed framework was evaluated on 135 independent retinal fundus images from the DRIVE, STARE, CHASE_DB1, HRF, and LES-AV datasets using an eroded field-of-view protocol. It achieved an image-weighted Dice coefficient of 0.7104 (95% bootstrap confidence interval: 0.7005–0.7205), an IoU of 0.5539, a sensitivity of 0.7544, a specificity of 0.9597, a balanced accuracy of 0.8570, and a clDice score of 0.7395. Compared with fixed majority voting, the proposed adaptive late fusion strategy significantly improved segmentation performance in 128 of 135 test images, yielding a mean Dice improvement of 0.0270 (paired Wilcoxon, Holm-adjusted p = 7.67 × 10⁻²¹). Although segmentation performance remains limited for complex pathological images, particularly in the HRF dataset, the proposed framework demonstrates consistent cross-dataset performance, transparent decision-making, and strong reproducibility, providing an effective alternative when interpretable, training-free retinal vessel segmentation is required.