Corneal Staining Grading via HSV Hue Quantification
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Solution Overview
Problem
Existing methods for grading corneal fluorescence in images are subjective and prone to errors, lacking sensitivity to detect small changes in corneal epithelial conditions, leading to inconsistencies and inaccuracies in assessing disease severity and treatment efficacy.
Innovation Solution
A computer-based analysis system that processes digital images of the cornea using polar coordinate-based color spaces to quantify fluorescence, excluding artifacts and assigning standardized scores based on hue values, allowing for objective evaluation of corneal staining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective visual grading methods are used to assess corneal fluorescence, then the evaluation process is simple and quick, but the measurement precision and reliability are poor due to human error and inconsistency
Solution Approach 1:
The patent replaces the manual visual grading mechanism with an automated computer-based image analysis system. The system uses software algorithms to objectively quantify corneal fluorescence by analyzing digital images, converting subjective visual assessment into precise numerical measurements. This substitution eliminates human variability while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent transforms the grading process by changing from qualitative visual parameters to quantitative numerical parameters. It uses color space conversion (RGB to HSV) and calculates specific hue values to represent corneal staining intensity. This parameter transformation enables precise measurement and objective comparison of fluorescence levels across different images and time points.
2Reliability
If automated image analysis is implemented to objectively quantify corneal fluorescence, then measurement precision and reliability improve, but the device complexity and processing requirements increase
Solution Approach 1:
The patent segments the image processing task into distinct computational steps: artifact detection and exclusion, evaluation area definition, color space conversion (RGB to HSV), hue value calculation, and score assignment. This segmentation allows each function to be performed by dedicated software modules, improving reliability through systematic processing while managing complexity through modular design.
Solution Approach 2:
The system reliably quantifies corneal fluorescence by transforming image data through parameter changes: converting from RGB color space to HSV color space, calculating hue values as the primary metric, and normalizing scores to a standardized range. These parameter transformations ensure consistent, objective measurements that are reproducible across different images and observers.
3Measurement precision
If artifact exclusion processing is applied to define evaluation areas, then the measurement precision improves by eliminating false readings, but the processing time and complexity increase
Solution Approach 1:
The patent performs artifact detection and exclusion as a preliminary step before quantitative analysis. By automatically identifying and masking artifact regions (such as specular reflections or out-of-focus areas) before calculating hue values, the system ensures that only valid corneal tissue is evaluated. This preliminary action prevents contamination of results while streamlining the overall process through automation.
Solution Approach 2:
The system employs self-service processing where the software automatically detects artifacts, defines evaluation boundaries, and performs quantification without manual intervention. This automation reduces processing time compared to manual artifact identification while maintaining high measurement precision through consistent application of detection algorithms across all images.
4Measurement precision
If quantitative analysis methods are used to detect small changes in corneal epithelial conditions, then the sensitivity improves, but the difficulty of detecting and measuring increases due to the need for precise parameter calculation
Solution Approach 1:
The patent enhances detection sensitivity by changing the measurement parameter from overall image intensity to hue value in HSV color space. This parameter transformation isolates the green fluorescence signal from corneal staining, enabling detection of subtle changes that would be imperceptible in raw RGB images. The hue parameter specifically captures the characteristic green color of fluorescein-stained corneal epithelium, providing high sensitivity for monitoring disease progression and treatment response.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, repeatable, and quantitative assessment of corneal epithelial conditions, facilitating the detection of small changes and improving the reliability of disease diagnosis and treatment monitoring.
Implementation Method 1
obtain a digital image of the cornea stained with a tracer material
Data Source
Figure 1
Figure 2A~2B
Figure 2C~2D
AI summary
The technology described in this document can be embodied in systems and computer-implemented methods for determining a score representing an amount of staining of the cornea. The methods include obtaining a digital image of the cornea stained with a tracer material, receiving a selection of a portion of the image, and processing, by a processing device, the selection to exclude areas with one or more artifacts to define an evaluation area. For each of a plurality of pixels within the evaluation area, a plurality of Cartesian color components are determined and a hue value in a polar coordinate based color space is calculated from the components. An amount of staining of the cornea is then determined as a function of the hue value. The methods also include assigning a score to the evaluation area based on the amount of staining calculated for the plurality of pixels.