Ocular Surface Imaging System for Corneal Defect Detection
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Solution Overview
Problem
Current methods for diagnosing ocular surface diseases like dry eye face challenges due to variability and unreliability in visual examinations, interference from Purkinje images, limited depth of field in imaging systems, and difficulty in separating surface stains from background fluorescence, leading to inconsistent and inaccurate diagnoses.
Innovation Solution
The use of filtered light sources and imaging systems that isolate specific wavelength ranges or polarizations to excite and detect contrast agents on the ocular surface, combined with optical systems that increase depth of field and Fourier image processing to capture full-field, in-focus images, allowing for quantitative analysis of contrast agent distribution without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual examination methods are used for diagnosing ocular surface diseases, then diagnostic capability is provided, but reliability and consistency of diagnosis deteriorate due to human bias and error
Solution Approach 1:
The patent replaces manual visual examination with an automated imaging and analysis system. A camera captures images of the ocular surface after staining, and computer algorithms automatically analyze the images to detect and quantify surface defects. This substitution of mechanical/manual examination with automated optical and computational systems eliminates human bias and improves diagnostic reliability and consistency.
2Measurement precision
If standard imaging systems with narrow depth of field are used to capture corneal images, then image capture is achieved, but measurement precision deteriorates because stained objects cannot be in sharp focus across the curved corneal surface
Solution Approach 1:
The patent addresses the depth of field limitation by capturing multiple images at different focal planes (different z-depths) and then combining them through image processing techniques such as focus stacking or computational refocusing. This transforms a single 2D image with limited depth of field into a composite image where objects across the entire curved corneal surface appear in sharp focus, thereby improving measurement precision without requiring complex optical hardware modifications.
3Object-affected harmful factors
If spectral filtering methods are used to block reflected light, then interference from Purkinje images is reduced, but image analysis difficulty increases due to incomplete separation of stain signal from background fluorescence
Solution Approach 1:
The patent employs multiple parameters to distinguish stain signal from background: (1) Spectral parameters - using specific wavelength bands where the stain fluoresces and background does not; (2) Spatial parameters - analyzing the distribution patterns and morphological characteristics of stained defects versus background; (3) Temporal parameters - capturing images at different time points after staining to observe signal evolution. By combining multiple parameter changes, the system achieves both removal of specular interference and accurate stain detection.
4Measurement precision
If quantitative image analysis is performed on ocular surface images, then objective measurement is achieved, but analysis accuracy deteriorates due to presence of unwanted specular images and background fluorescence
Solution Approach 1:
The patent extracts the desired stain signal from the composite image by applying various filtering and segmentation techniques. Background fluorescence and specular reflections are identified and removed through thresholding, region-based segmentation, or machine learning-based classification. The remaining signal represents the actual stain uptake by ocular surface defects, enabling accurate quantitative measurement of disease severity.
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
This approach enables accurate, automated, and objective detection of ocular surface defects, reducing human error and enhancing diagnostic reliability by eliminating specular images and improving image clarity, thereby facilitating precise measurement and monitoring of corneal surface diseases.
Implementation Method 1
the light is transmitted in a first predetermined wavelength range by an illumination filter positioned between the light source and the ocular surface, and wherein the light in the first predetermined wavelength range excites a contrast agent bound to defects on the ocular surface
Implementation Method 2
detecting a light signal emitted from the contrast agent, wherein the light signal is transmitted in a second predetermined wavelength range by an imaging filter to an image capture device
Implementation Method 3
the image capture device comprises an optical system with appropriate optics to increase depth of field in the image and transmits full field in focus image signals
Data Source
AI summary
The invention provides apparatuses and methods for detecting corneal surface defects. The methods and/or an apparatus of the invention can be used to detect corneal surface diseases, such as dry eye.