Table 1.
Summary of limitations in traditional vs. deep learning-based segmentation methods.
Table 2.
Performance analysis of different edge preserving filters.
Table 3.
Comparison between AGF and OAGF in practical deployment terms.
Fig 1.
Block diagram of retinal image segmentation and enhancement using proposed OAGF.
Fig 2.
Retinal image segmentation across six test cases using proposed OAGF.
Fig 3.
Subjective assessment of existing and proposed OAGF based segmentation approaches.
Table 4.
Quantitative analysis of proposed and existing methods for DRIVE and STARE datasets.
Table 5.
Statistical significance test analysis.
Fig 4.
Metrics Evaluation with DRIVE and STARE Datasets.
Fig 5.
Visual failure case comparison.
Red box shows zoomed region where thin vessels are either missed or disconnected in the proposed method due to low contrast and anatomical ambiguity.
Table 6.
Ablation Study and Component-Level Analysis.
Fig 6.
Sensitivity analysis of OAGF to key hyperparameters.
Fig 7.
Subjective assessment of existing and proposed OAGF based enhancement.
Table 7.
Comparison of existing and proposed OAGF techniques through objective assessment.
Table 8.
Comparison of existing and proposed OAGF techniques through objective assessment.