Anterior Segment OCT Distortion Correction With Corneal Shape Estimation
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Solution Overview
Problem
Existing anterior eye segment analysis techniques using optical coherence tomography (OCT) face limitations in imaging range, leading to incomplete imaging of the cornea, which hinders accurate refraction correction and analysis, such as corner angle analysis.
Innovation Solution
An ophthalmic apparatus that includes an image acquiring unit, a corneal shape estimating processor, and an image correcting processor to estimate and correct distortion in anterior segment images, even when a part of the cornea is not imaged, using curve fitting and robust estimation algorithms like RANSAC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wide-area B-scan is applied to image a large area of anterior eye segment, then imaging coverage is improved, but image quality and completeness of corneal imaging deteriorates due to depth direction limitations
Solution Approach 1:
The patent segments the corneal imaging task into two parts: the visible corneal region from the OCT image and the missing central corneal region. By identifying the visible anterior corneal surface region and estimating the missing part separately, the system can process each segment with appropriate methods, thereby resolving the contradiction between wide-area coverage and complete corneal imaging.
2Measurement precision
If refraction correction is performed to improve analysis accuracy, then measurement precision is improved, but the process becomes impossible when corneal image is incomplete
Solution Approach 1:
The patent performs preliminary action by estimating the shape of the missing corneal part before refraction correction is applied. The corneal shape estimating processor reconstructs the missing central corneal region using curve fitting based on the visible corneal surface, enabling subsequent refraction correction to proceed even when the original image is incomplete.
3Loss of information
If curve fitting is applied to estimate missing corneal shape, then completeness of corneal data is improved, but processing complexity increases
Solution Approach 1:
The patent creates a simplified copy or representation of the missing corneal shape using mathematical curves (ellipse or parabola) fitted to the visible corneal surface data. Instead of attempting to reconstruct the full complex three-dimensional corneal structure, the system uses simple parametric curves to represent the essential shape characteristics, thereby reducing processing complexity while maintaining data completeness.
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 refraction correction and analysis of anterior eye segments by estimating and correcting image distortions, allowing for precise measurement and imaging of the cornea, even when a part is missing.
Implementation Method 1
anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning
Data Source
AI summary
An ophthalmic apparatus of an aspect example includes an image acquiring unit, a corneal shape estimating processor, and a first image correcting processor. The image acquiring unit is configured to acquire an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning. The anterior segment image includes a missing part corresponding to a part of a cornea. The corneal shape estimating processor is configured to estimate a shape of the missing part of a cornea image by analyzing the anterior segment image acquired by the image acquiring unit. The first image correcting processor is configured to correct distortion of the anterior segment image based at least on the shape of the missing part estimated by the corneal shape estimating processor.