Automated Quantification of Hyphema Using Multi-Method Computer Vision: A Robust Approach for Clinical Monitoring

This paper presents an automated computer vision system for quantifying hyphema (blood accumulation in the eye's anterior chamber) from patient-captured photographs. The system employs a novel multi-method ensemble approach combining HSV color filtering, adaptive intensity thresholding, and intelligent region growing with a 2-of-3 voting fusion mechanism. Key features include: Fully automated system requiring no manual intervention Real-time processing (0.03-3.7 seconds based on images resolutions) Robust performance across varying lighting conditions and image quality 100% iris localization success on test dataset (resolution >250×200 pixels) Estimated 60-70% quantification accuracy compared to clinical assessment The method provides quantitative percentage measurements (0-100%) that offer finer granularity than traditional categorical grading (Grade I-IV), enabling precise tracking of healing progression and early detection of complications like rebleeding. Technical Implementation: Python 3.x with OpenCV 4.x, Spring boot for backend and Thymleaf for UI, deployable on consumer hardware without GPU acceleration. Full methodology, algorithms, validation results, and integration guidelines included. Note: This work requires further validation through prospective clinical trials with ground-truth measurements to confirm accuracy and generalizability across diverse patient populations.