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

This paper present an automated computer vision system for quantifying hyphema coverage using a novel multi-method ensemble approach that combines color-based detection, intensity-based thresholding, and adaptive region growing. The proposed algorithm has been integrated into a full-stack web application (Spring Boot backend, Thymeleaf frontend, PostgreSQL database), though this paper focuses exclusively on the computational pipeline. Our system quantify 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.