Retinal Vessel Differentiation via Diameter and Brightness Analysis

The patent transforms the artery-vein differentiation problem from a complex classification task into a parameter-based identification task. By calculating specific parameters (brightness difference, included angle, distance ratio) and comparing them against threshold values, the system achieves reliable automated differentiation without relying on machine learning models with poor generalizability. This parameter-change approach converts an unreliable automated process into a robust one.

The patent replaces the machine learning-based automated system with a rule-based geometric and photometric analysis system. Instead of using neural networks or other ML mechanisms, the invention employs deterministic calculations based on vessel brightness, spatial relationships, and geometric parameters, substituting the ML mechanism with a more reliable rule-based mechanism that maintains automation while improving robustness.

The patent implements an automated computer-based system that performs the measurement tasks that previously required manual visual estimation by physicians. The system automatically extracts vessel images, calculates diameters, identifies artery-vein pairs, and computes the diameter ratio without human intervention, making the measurement process self-service and eliminating the need for subjective visual assessment while maintaining ease of operation.

The patent creates a digital copy and representation of the retinal vessel structures through image processing and skeletonization. By working with processed digital representations rather than original complex images, the system enables precise automated measurement while maintaining operational simplicity. The copying and simplification of vessel structures into measurable parameters allows computers to perform tasks that were previously only feasible through visual estimation.

The patent segments the artery-vein differentiation task into distinct, manageable components: vessel extraction, diameter measurement, brightness calculation, angle computation, and pair identification. Each component is handled by a separate module with a specific function, breaking down what would otherwise require a complex monolithic machine learning algorithm into simpler, more maintainable segments that collectively achieve high differentiation accuracy without excessive device complexity.

The patent employs a multi-functional integrated system that performs vessel extraction, diameter measurement, brightness analysis, geometric calculation, and differentiation identification within a unified framework. This universal approach allows a single device to handle multiple tasks that would otherwise require separate specialized algorithms, reducing overall device complexity while maintaining high differentiation accuracy through coordinated multi-functionality.