1：Experimental Design and Method Selection:

The pipeline includes vessel enhancement and segmentation using multi-scale left-invariant derivative (LID) and locally adaptive derivative (LAD) filters in orientation scores, optic disc (OD) and fovea detection using super-elliptical filters, artery/vein classification using a supervised approach with genetic search-based feature selection and linear discriminant analysis (LDA), vessel width measurement using a multi-scale active contour technique, vessel tortuosity measurement using exponential curve fits in orientation scores, and fractal dimension (FD) measurement using box dimension, information dimension, and correlation dimension methods.

2：Sample Selection and Data Sources:

Seven datasets are used: MESSIDOR (1200 images), DRIVE (40 images), STARE (20 images), INSPIRE-AVR (40 images), NIDEK (200 images), CANON (120 images), and TOPCON (120 images). These include publicly available and private retinal fundus images with various resolutions and annotations.

3：List of Experimental Equipment and Materials:

Retinal images acquired using fundus cameras such as Topcon non-mydriatic retinograph, Canon CR5 non-mydriatic 3CCD camera, NIDEK AFC-230 non-mydriatic fundus camera, and Topcon NW

4：Software tools include MATLAB or similar for image processing, and manual annotation tools like 'RHINO'. Experimental Procedures and Operational Workflow:

3 Steps involve image preprocessing, vessel enhancement and segmentation, OD and fovea detection, artery/vein classification, biomarker extraction (width, tortuosity, FD), and validation against human annotations using performance metrics like sensitivity, specificity, accuracy, and AUC.

5：Data Analysis Methods:

Statistical analysis includes Mann-Whitney U tests, one-way ANOVA, Bland-Altman plots, and calculation of relative errors and coefficients of variation to assess robustness and accuracy.