Fundus Camera Auto Calibration Using Stereo Vision
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Solution Overview
Problem
Conventional fundus camera calibration and alignment methods require skilled operators and are prone to errors due to harsh transportation conditions and environmental factors, affecting the quality of fundus images.
Innovation Solution
A method and system for automatic calibration and alignment of a fundus camera using a stereo camera with a multi-planar calibration target, movable platform, and illumination source, which calculates and minimizes errors in axis alignment and illumination projection to ensure precise positioning and validation within predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fundus camera calibration methods are used with skilled operators, then alignment accuracy can be achieved, but operational complexity and time consumption increase
Solution Approach 1:
The system performs automatic calibration and alignment using the stereo camera and multi-planar calibration target without requiring skilled operators. The computer automatically calculates intrinsic and extrinsic parameters, determining the optimal position and orientation of the fundus camera through self-contained algorithms and validation processes.
Solution Approach 2:
The system performs preliminary calibration using the multi-planar calibration target with fiducial markers before actual fundus imaging. The stereo camera captures images of the calibration target to pre-determine camera parameters and validate alignment, ensuring accuracy is established in advance rather than during operation.
2Measurement precision
If manual calibration by skilled operators is performed, then alignment quality can be maintained, but productivity decreases
Solution Approach 1:
The system replaces manual mechanical alignment operations with an automated optical and computational system. The stereo camera captures images, and computer algorithms automatically calculate calibration parameters and validate alignment, substituting the mechanical skill-based process with an automated digital system that increases productivity while maintaining precision.
Solution Approach 2:
The system incorporates validation processes that compare calculated alignment parameters against expected values and predefined thresholds. This feedback mechanism ensures alignment quality is maintained while enabling rapid automated operation, as the system can quickly verify and adjust calibration without requiring skilled operator intervention for each adjustment.
3Loss of time
If calibration is performed using traditional methods, then setup time can be reduced, but reliability under harsh conditions deteriorates
Solution Approach 1:
The system uses a multi-planar calibration target with multiple fiducial markers arranged in three-dimensional space rather than traditional two-dimensional calibration patterns. This dimensional expansion provides more reference points for calculating camera parameters, making the calibration more robust to transportation conditions and environmental variations while maintaining rapid setup through automated processing.
Solution Approach 2:
The system performs preliminary validation of calibration parameters against predefined thresholds before actual fundus imaging begins. This advance verification ensures that calibration remains reliable under harsh transportation and environmental conditions, detecting and flagging any degradation before it affects imaging quality.
Data Source
AI summary
A method of validating alignment of an image sensor of a camera and an illumination projection for use in a system for automatically aligning a camera device which includes the camera, wherein the camera has an image sensor wherein a centre of the image sensor is identified; and the camera device includes an illumination source wherein a centre of an illumination projection is identified, and the camera device also includes a stereo camera in addition to the camera, the method comprising: calculating error between the centre of the image sensor and the centre of the illumination projection and validating alignment of the image sensor and the illumination projection if the error is within a predefined threshold.