Microscope Image Focus Measurement with Gradient-Aware Neural Analysis
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Solution Overview
Problem
Existing methods for determining the focusing quality of microscope images of biological cell substrates are prone to errors due to the presence of objects like particles, which can lead to incorrect focusing planes, resulting in blurred images and unreliable pattern detection.
Innovation Solution
A method utilizing a neural network that processes both image information and gradient information from a microscope image to determine a focusing measure, specifically by identifying and analyzing selected partial images from the gradient and microscope images, reducing complexity and enhancing reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional focusing methods are used to determine the focusing plane, then the focusing process is simple and quick, but the presence of particles or bubbles can lead to incorrect focusing planes and blurred images
Solution Approach 1:
The patent divides the microscope image into multiple partial images and processes them separately through the neural network. This segmentation allows the system to evaluate different regions independently, reducing the impact of particles or bubbles in specific areas while maintaining overall focusing quality assessment.
Solution Approach 2:
The patent introduces a gradient image as an intermediary representation between the original microscope image and the focusing quality assessment. The gradient image highlights edges and transitions, providing the neural network with enhanced features for detecting focusing quality while being less susceptible to interference from particles or bubbles.
2Measurement precision
If all image information is processed to determine focusing quality, then the assessment is comprehensive, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent processes only selected partial images rather than the entire microscope image through the neural network. This partial action approach maintains sufficient precision for focusing quality assessment while significantly reducing computational complexity and processing time compared to analyzing the complete image.
Solution Approach 2:
By segmenting the image into partial regions and selectively processing them, the system achieves a balance between comprehensive assessment and computational efficiency. The segmentation enables parallel processing and reduces the data volume requiring intensive neural network computation.
3Productivity
If gradient image information is used alone to determine focusing quality, then the processing is fast and simple, but the presence of particles can lead to false positive focusing quality assessments
Solution Approach 1:
The patent merges gradient image information with original image information by feeding both into the neural network simultaneously. This combination allows the system to leverage the speed advantages of gradient-based processing while using the original image context to verify and correct potential false assessments caused by particles or bubbles.
Solution Approach 2:
The neural network receives both gradient and original image information as feedback inputs, allowing it to cross-validate findings and adjust its focusing quality assessment. This feedback mechanism enables the system to maintain high processing speed while improving reliability by detecting when gradient information alone might be misleading.
Data Source
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AI summary
A method is proposed for determining a focusing measure of a microscope image, wherein the microscope image represents an image of a biological cell substrate, the method comprising: providing the microscope image, determining a gradient image based on the microscope image, processing image information of the gradient image and image information of the microscope image by means of a neural network to determine the focusing measure, wherein the focusing measure indicates a quality of a focus in the microscope image with respect to a cell substrate plane of the cell substrate.