Fundus Imaging Motion Correction via Characteristic Point Tracking
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Solution Overview
Problem
Current ophthalmologic imaging systems, particularly those using scanning imaging systems, face challenges in accurately detecting and correcting for eye movements during image acquisition, leading to distorted images and reduced precision in three-dimensional imaging.
Innovation Solution
An ophthalmologic apparatus and method that extracts characteristic images from a first fundus image and acquires a second fundus image at a different time, allowing for the calculation of rotational movements and correction of image distortions, enabling accurate detection of complex eye movements and improving image precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a scanning imaging system is used to acquire fundus images, then high contrast and high resolution are achieved, but image distortion occurs due to eye movements during the scanning period
Solution Approach 1:
The patent extracts characteristic images (templates) from the fundus image at the beginning of the scanning process. These templates serve as reference points for detecting eye movements during scanning, allowing preliminary preparation that enables subsequent correction of motion distortion while maintaining high resolution imaging.
Solution Approach 2:
The system continuously monitors the positions of characteristic points in the fundus image during scanning and uses this feedback information to detect eye movements. This real-time feedback enables dynamic correction of image distortion caused by eye movements, maintaining both high resolution and minimal distortion.
2Measurement precision
If the scanning imaging system is used for eye movement detection, then high precision detection is possible, but time lag in single acquired image causes distortion
Solution Approach 1:
The patent performs preliminary extraction of characteristic images and identification of characteristic points before completing the full scanning process. This preliminary action establishes reference data that enables accurate eye movement detection without requiring the entire image acquisition to be completed first, thereby reducing the effective time lag impact.
Solution Approach 2:
The system dynamically adjusts the detection process by continuously tracking characteristic points throughout the scanning sequence. Rather than relying on a single static image with inherent time lag, the dynamic tracking of multiple characteristic points across the scanning sequence enables accurate eye movement detection that compensates for the temporal dispersion in image acquisition.
3Manufacturing precision
If five parameters (translation factors, rotation factor, magnification factors) are used for position correction, then image alignment is achieved, but accurate detection of complicated eye movements remains difficult
Solution Approach 1:
The patent segments the eye movement detection into multiple independent components by tracking individual characteristic points separately. Each characteristic point provides independent information about eye movement, and these segmented measurements are then combined to achieve comprehensive detection of complicated movements, overcoming the limitations of global parameter-based methods.
Solution Approach 2:
The patent transitions from using global image parameters (translation, rotation, magnification) to a point-based dimensional approach by extracting and tracking multiple characteristic points. This dimensional change from global to local measurements enables more accurate detection of complicated eye movements, as each point provides independent spatial information that collectively captures complex motion patterns.
Data Source
AI summary
To accurately detect a movement of an object based on an image distorted by the movement of an eye to be inspected, which is acquired by a scanning imaging system, provided is an ophthalmologic apparatus including: an extraction means for extracting a plurality of characteristic images from a first fundus image of the eye to be inspected; a fundus image acquisition means for acquiring a second fundus image of the eye to be inspected during a period different from a period during which the first fundus image is acquired; and a calculation means for calculating at least a rotational movement of the eye to be inspected based on the plurality of characteristic images and the second fundus image.