Retinal Vessel Diameter Tracking for Stroke Risk Assessment
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Solution Overview
Problem
Current methods for analyzing retinal vessels from digital images are prone to high measurement uncertainty, inter-individual variability, and subjective errors, making them less effective for discriminating between healthy and at-risk vessel states, particularly for stroke risk assessment, and are time-consuming.
Innovation Solution
A method and apparatus for retinal vessel analysis that automatically determine vessel diameters and calculate parameters by selecting adjoining vessel segments, correlating them to type, and storing data for comparison measurements, allowing for precise and reproducible assessment of vascular changes over time, reducing manual effort and subjective influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual and semi-manual methods are used for retinal vessel analysis, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to high measurement uncertainty and subjective systematic and random errors
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated digital image processing system. The system uses computer-based algorithms to automatically detect, track, and measure vessel diameters along the retinal vasculature, eliminating manual intervention while maintaining measurement accuracy and consistency.
Solution Approach 2:
The system enables self-service measurement by automatically performing vessel detection, segmentation, and diameter calculation without requiring manual operation. The automated tracking algorithm independently identifies vessel centers and calculates diameters based on image intensity profiles, providing reliable measurements without subjective human error.
2Quantity of substance
If comprehensive vessel measurements are performed manually, then complete data collection is achieved, but time consumption increases significantly
Solution Approach 1:
The automated system enables continuous measurement of vessel diameters along the entire retinal vasculature without interruption. The tracking algorithm continuously calculates diameter values at multiple points along vessel paths, allowing comprehensive data collection in a single automated operation rather than through time-consuming manual measurements.
Solution Approach 2:
The system replaces time-consuming manual measurement processes with automated digital image analysis. Computer algorithms rapidly process fundus images to extract vessel geometry and calculate diameters, reducing assessment time from hours of manual work to minutes of automated computation.
3Measurement precision
If automated vessel segmentation and tracking are implemented, then measurement precision and reproducibility improve, but device complexity increases
Solution Approach 1:
The system segments the retinal vasculature into discrete vessel paths by identifying vessel centers and tracking their courses through the fundus image. This segmentation divides the complex vascular network into manageable segments that can be individually measured and analyzed, improving precision while organizing the computational task.
Solution Approach 2:
The patent introduces intermediate computational steps including vessel centerline detection, path tracking, and intensity profile analysis as mediators between the raw image and final diameter measurement. These intermediary processes break down the complex measurement task into sequential operations that improve accuracy while maintaining systematic control.
4Reliability
If individual follow-ups and comparisons to healthy test groups are performed, then diagnostic capability is enhanced, but time consumption and measurement uncertainty increase
Solution Approach 1:
The system performs preliminary automated measurement and establishes baseline vessel diameter values during the initial examination. These pre-measured values are stored and can be automatically compared with follow-up examinations, eliminating the need for time-consuming manual re-measurement and enabling rapid longitudinal assessment of vascular changes.
Data Source
AI summary
It is the object of a method and apparatus for retinal vessel analysis based on digital images to enhance the ability to discriminate between healthy vessel states and at-risk vessel states while reducing manual effort and saving time in order to allow individual vascular risk, particularly stroke risk, to be determined in a more reliable manner and with fewer subjective systematic and random errors. The vessel segment diameter, type of vessel and the image coordinates are determined for a series of adjoining vessel segments along vessel portions in a measurement zone surrounding the papilla and are stored by vessel segment with reference to the evaluated image, to a reference image recorded with a time offset, and to a displacement vector that is determined for the vessel segment between the reference image and an evaluated comparison image. Comparison measurements are carried out only on identical vessel segments already measured in the reference image. The correlation of vessel segments to vessel portions and to vessel type is adopted intact from the reference image. The stored data sets for the vessel segments of the reference image and comparison images provide a progression of coordinate-oriented vessel segment diameters for all measured vessel segments as basis for determining parameters and presenting them in a spatially resolved progression, e.g., in progress images.