Retinal Image Processing Techniques for Vascular Feature Extraction

This document presents image processing techniques utilized for extracting vascular features from retinal images. These techniques are crucial for identifying and analyzing blood vessel structures in fundus images. By implementing algorithms such as morphological operations, vessel segmentation using matched filters or neural networks, and skeletonization methods, key vascular characteristics including shape patterns, branch lengths, tortuosity metrics, and bifurcation points can be quantified. The feature extraction process typically involves preprocessing steps like contrast enhancement using adaptive histogram equalization, followed by vessel segmentation algorithms that employ Hessian matrix-based Frangi filtering for tubular structure detection. These computational methods provide ophthalmologists and researchers with comprehensive quantitative data about retinal vasculature, enabling early diagnosis and monitoring of ocular diseases like diabetic retinopathy and glaucoma. The application of these image processing techniques significantly contributes to improved ocular health management by facilitating precise vascular analysis and pathological assessment.