Anterior Segment OCT Quality Assessment for Cornea and Iris
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Solution Overview
Problem
Existing optical coherence tomography (OCT) anterior segment analysis techniques struggle to accurately depict both the cornea and iris with sufficient image quality, leading to inaccuracies in corner angle analysis and other anterior segment measurements, resulting in prolonged examination times and reduced precision.
Innovation Solution
An ophthalmic apparatus equipped with image processing units to identify and assess the quality of multiple anterior segment parts, such as the cornea and iris, by setting assessment regions within and outside these structures, applying edge detection, and correcting pixel aspect ratios to enhance image quality for precise parameter calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCT scanning is performed without image quality assessment, then the examination process is simple and quick, but the image quality of cornea and iris cannot be guaranteed, leading to inaccurate corner angle analysis
Solution Approach 1:
The patent divides the anterior segment image into multiple part images corresponding to different anatomical structures (cornea, iris, etc.). Each part image is independently assessed for quality, allowing targeted evaluation of critical regions without requiring complex global image processing.
Solution Approach 2:
The patent performs image quality assessment of part images before conducting corner angle analysis. By preliminarily evaluating whether the cornea and iris are depicted with sufficient quality, the system determines whether the image is suitable for accurate measurement, avoiding post-hoc corrections.
2Measurement precision
If OCT scanning is re-executed due to insufficient image quality, then accurate corner angle analysis can be achieved, but examination time increases
Solution Approach 1:
The patent performs image quality assessment before corner angle analysis to determine in advance whether the image quality is sufficient. This preliminary check prevents unnecessary re-scanning by identifying suitable images upfront, thereby reducing examination time while maintaining accuracy.
Solution Approach 2:
The system automatically assesses image quality and determines suitability for analysis without requiring manual review or repeated scanning. The automated quality assessment enables the system to self-judge whether the captured image meets the required standards, streamlining the workflow.
3Reliability
If image quality assessment of multiple part images is performed, then the reliability of anterior segment analysis is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the anterior segment image into multiple part images (cornea, iris, etc.) and performs quality assessment on each segment independently. This segmentation approach improves reliability by ensuring each critical structure meets quality standards, while the modular nature keeps processing manageable.
Solution Approach 2:
The patent employs a universal image quality assessment methodology that can be applied to multiple different part images (cornea, iris, and other anterior segment structures). This multi-functional assessment system improves overall analysis reliability without requiring separate complex assessment procedures for each structure.
Data Source
AI summary
In the ophthalmic apparatus 1 of an aspect example, the image acquiring unit (the fundus camera unit 2, the OCT unit 100, the image data constructing unit 220) acquires an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by OCT scanning. The part image identifying processor 231 performs identification of two or more part images respectively corresponding to two or more parts of the anterior segment from the anterior segment image acquired. The part image assessing processor 232 performs image quality assessment of each of the two or more part images. The anterior segment image assessing processor 233 performs image quality assessment of the anterior segment image based on two or more pieces of assessment data respectively obtained for the two or more part images.