Riboflavin Penetration Depth Detection in Cornea
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack a reliable and accurate way to quantify the penetration depth of riboflavin in corneas, which is crucial for the effectiveness of corneal collagen cross-linking treatments.
Innovation Solution
A method and apparatus using image processing techniques, specifically converting RGB images to CIEL*a*b* color space, extracting luminance components, performing binarization and Blob analysis, and curve fitting to determine the riboflavin penetration depth by identifying and isolating the corneal region and applying K-mean clustering for noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing methods are used to detect riboflavin penetration depth, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses color space conversion (RGB to CIEL*a*b*) as an intermediary process to transform the original image data into a format where riboflavin penetration can be more easily detected and measured. This intermediary transformation enables precise measurement without requiring complex direct detection methods
Solution Approach 2:
The patent replaces complex mechanical or physical measurement methods with computational image processing techniques. By using software-based analysis of color information and luminance components, the system achieves precise penetration depth measurement without complex hardware interventions
2Measurement precision
If color space conversion and image processing are applied, then measurement accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential luminance component L* from the CIEL*a*b* color space, which contains the critical information for riboflavin penetration detection. By focusing on this single component rather than processing all color information, the system maintains high measurement accuracy while reducing processing time
Solution Approach 2:
The patent segments the image processing task into distinct stages: color space conversion, binarization, Blob analysis, and curve fitting. This segmentation allows each step to be optimized independently and enables parallel processing where applicable, reducing overall processing time while maintaining accuracy
Data Source
AI summary
A method for detecting a penetration depth of riboflavin in a cornea includes acquiring an optical section of RGB colors and converting it into an image in a CIEL*a*b* color space, extracting a luminance component L* in the CIEL*a*b* color space, performing binarization and Blob analysis to obtain a non-corneal-region-free image; based on the non-corneal-region-free image and the luminance component L* image, extracting boundary points of anterior and posterior corneal surfaces and performing curve fitting, so as to obtain anterior and posterior corneal surface curves; performing image fusion on the anterior and posterior corneal surface curves into the CIEL*a*b* color space to obtain a cornea-only CIEL*a*b* color image; in the cornea-only CIEL*a*b* color image, locating a riboflavin-penetrating region and partitioning the riboflavin-penetrating region into riboflavin-penetrating sites, and performing curve fitting and noise reduction on the riboflavin-penetrating sites to obtain riboflavin the penetration depth and the anterior and posterior curves.