Ophthalmologic Visualization System Limbus Pupil Detection
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Solution Overview
Problem
In ophthalmologic surgery, accurately determining the position and radius of the limbus and pupil is challenging due to eye movement and blood from injured vessels, which can obscure the structures, making it difficult for surgeons to locate the correct incision sites during procedures like LASIK and cataract surgery.
Innovation Solution
The method involves correlating digital images of the eye with ring-shaped comparison objects of varying radii and centers to find a local best match, using convolution with ring filters to determine the position and radius of the limbus and pupil, even in the presence of impairments like surgical instruments, by leveraging the ring-shaped transition objects of brightness in the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis methods are used to detect limbus and pupil positions, then the detection process is simple, but the detection accuracy deteriorates due to eye movement and blood obscuring the structures
Solution Approach 1:
The detection method is segmented into multiple processing stages: initial detection phase to establish baseline positions, tracking phase to follow eye movements, and validation phase to filter unreliable detections. This segmentation allows the system to maintain high accuracy without requiring overly complex algorithms to run continuously at full capacity.
Solution Approach 2:
The system performs preliminary detection to establish initial positions of limbus and pupil before surgery begins. These preliminary measurements create reference data that guides subsequent tracking, reducing the complexity of real-time detection by having pre-established benchmarks to compare against.
2Reliability
If multiple image processing algorithms are applied to improve detection reliability, then detection reliability improves, but processing time increases
Solution Approach 1:
The system applies multiple processing algorithms periodically rather than continuously. Full multi-algorithm processing is performed at key moments (initial detection, after significant eye movements, when confidence drops below thresholds), while between these events the system uses lighter-weight tracking algorithms, thus maintaining reliability without constant heavy processing.
Solution Approach 2:
The system uses feedback from detection confidence metrics to dynamically adjust processing intensity. When detection confidence is high, simpler algorithms suffice. When confidence drops or conditions change (blood obscuration, eye movement), the system activates more robust algorithms, creating a feedback loop that maintains reliability while minimizing unnecessary processing time.
3Manufacturing precision
If real-time tracking is implemented to follow eye movements, then surgical precision improves, but system complexity increases
Solution Approach 1:
The tracking system is designed to be dynamic rather than static, automatically adjusting its behavior based on detected conditions. The system transitions between tracking modes (initialization, active tracking, re-initialization) based on real-time inputs, allowing surgical precision to be maintained without requiring permanently complex system architecture.
Solution Approach 2:
The tracking system performs self-correction and self-validation, using its own detection outputs to verify and adjust its tracking accuracy. This self-service capability reduces the need for external calibration and manual intervention, maintaining high precision while managing system complexity through autonomous operation.
4Measurement precision
If robust edge detection algorithms are used to identify limbus and pupil boundaries, then detection accuracy improves, but false detections increase under distracting conditions
Solution Approach 1:
The system introduces intermediary validation steps between edge detection and final identification. Detected boundaries are not immediately accepted but are validated against multiple criteria (geometric consistency, brightness profile matching, anatomical plausibility). This intermediary layer filters out false detections from distracting conditions while preserving true boundary detections.
Solution Approach 2:
The system dynamically changes detection parameters based on image conditions. When distracting elements are detected (blood, instruments, poor lighting), the algorithm adjusts parameters such as threshold values, search window sizes, and feature weights to maintain accurate boundary detection while rejecting false positives from the distracting conditions.
Data Source
AI summary
A method determines the position and/or radius of the limbus and/or the position and/or radius of the pupil of a patient eye. In the method, an image of the patient eye is obtained and a plurality of different ring-shaped comparison objects having respective radii and respective centers are provided. The image is correlated with the plurality of comparison objects to yield a local best match between the image and the comparison objects when there is a coincidence of one of the ring-shaped comparison objects and a ring-shaped jump in brightness in the image having the same radius and the same center. The comparison objects having a local best match with the image are determined. Thereafter, the position of the center of the comparison object having a local best match with the image is selected as the position of the center of the limbus and/or the position of the center of the pupil.