Centroid Calculation Using Ghost Spot Masking for Aberrometers
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Solution Overview
Problem
Current aberrometers face challenges in accurately measuring higher-order wavefront aberrations due to issues like scattered light, ghost images, edge distortion, and internal reflections, which affect the precision of centroid calculation and subsequent wave aberration computation, especially in pathological eyes with severe aberrations.
Innovation Solution
A method and system for determining the centroid coordinate of image spots using a search region with threshold intensity values to differentiate between actual and ghost spots, and adjusting the integration region based on these thresholds to improve centroid calculation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional centroid calculation algorithms are used, then the computation is simple, but the measurement precision deteriorates due to ghost images and scattered light
Solution Approach 1:
The patent divides the image processing into distinct stages: background subtraction to remove scattered light, ghost spot identification and masking, and then centroid calculation. This segmentation allows each stage to address specific problems independently, improving overall measurement precision without excessive complexity
Solution Approach 2:
The patent extracts and removes harmful elements from the image data before centroid calculation. Specifically, ghost spots are identified through template matching and extracted via masking, while scattered light is removed through background subtraction. This extraction of harmful factors enables accurate centroid computation
2Object-affected harmful factors
If high band-pass filtering is applied to reduce noise, then background noise is reduced, but edge distortion increases and feature size/shape is altered
Solution Approach 1:
The patent uses a disposable background model created from multiple frames that is subtracted from each individual frame. This background model acts as a temporary, frame-specific correction that removes scattered light and noise without requiring complex filtering operations on the actual image data, preserving edge integrity
Solution Approach 2:
The patent introduces a background model as an intermediary element between the raw image and the centroid calculation. This background model mediates the removal of scattered light and noise, allowing the actual image features to remain intact for accurate centroid computation without direct application of aggressive filters
3Measurement precision
If the integration region is expanded to include more pixels for better centroid accuracy, then measurement precision improves, but ghost spots and scattered light have greater influence
Solution Approach 1:
The patent extracts and removes ghost spots from the image data through template matching and masking before centroid calculation. By removing these harmful elements, the integration region can be expanded to include more pixels for better precision without incorporating ghost spot data that would corrupt the centroid calculation
Solution Approach 2:
The patent performs preliminary background subtraction and ghost spot removal before the centroid calculation step. This preliminary action cleans the image data, allowing subsequent centroid computation to use a larger integration region with confidence that harmful factors have already been eliminated
4Measurement precision
If multiple image spots are included in centroid calculation to improve statistics, then measurement precision improves, but accuracy deteriorates when spots are occluded or displaced
Solution Approach 1:
The patent implements feedback through iterative ghost spot detection and validation. The system calculates initial centroids, uses them to generate expected spot positions, compares actual detections with expectations, and refines the identification by masking and re-evaluating. This feedback loop ensures that only valid spots are included in final centroid calculations, maintaining reliability even in pathological cases
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
Enhances the accuracy of wavefront aberration measurement by reducing errors from ghost spots and background noise, leading to more precise computation of wave aberrations and improved corrective applications in ophthalmology.
Implementation Method 1
a Hartmann-Shack sensor, which includes an array of lenslets that form an array of aerial image spots and focus the spots onto a detector
Data Source
AI summary
A method for determining a centroid coordinate of an image spot, comprising a) determining a search region having a border, b) identifying a first pixel in the search region, the pixel having a first intensity value, c) determining an upper threshold intensity value greater than the first intensity value, d) searching for a second pixel within the search region having an intensity value that is greater than the upper threshold intensity value, and e) upon finding the second pixel, designating the first pixel an integration region center.