Flow Particle Image Analysis Focal Adjustment
Solution Overview
Problem
Conventional flow type particle image analysis devices face challenges in accurately and efficiently adjusting the in-focus position and checking the displacement of the in-focus position and thickness of the sample liquid, particularly due to issues with particle size variation and the need for time-consuming data collection and complex neural network learning.
Innovation Solution
The method involves using spherical standard particles to calculate density-based feature parameter values, such as the ratio and difference between central and outline density values, for focal adjustment and thickness assessment, allowing for efficient in-focus point adjustment and accurate monitoring of the sample liquid's flow characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional focal adjustment methods using area values or covariance are employed, then focal adjustment can be performed, but the adjustment accuracy deteriorates when particle size varies or the relationship between parameter and focal position is non-monotonic
Solution Approach 1:
The invention changes the parameter used for focal adjustment from area values or covariance to density-based feature parameter values. Specifically, it calculates the ratio or difference between density values at the center and outline of particle images, which provides a monotonic relationship with focal position that is independent of particle size, thereby improving both accuracy and reliability
Solution Approach 2:
The invention introduces density-based feature parameter values as an intermediary between the particle images and focal position determination. By using density ratios or differences as the measurement intermediary, the system achieves accurate focal adjustment without being affected by particle size variations or non-monotonic relationships
2Extent of automation
If neural network learning methods are used for focal adjustment, then automatic adjustment can be achieved, but the time required for data collection and learning increases significantly
Solution Approach 1:
The invention replaces the complex neural network learning system with a simpler mathematical calculation system based on density ratios or differences. This substitution eliminates the need for extensive data collection and training while maintaining automatic focal adjustment capability, significantly reducing the time required
Solution Approach 2:
The system uses standard particles already present in the sample liquid for focal adjustment without requiring separate calibration procedures or additional training data. The density-based parameters are calculated directly from captured images, enabling the system to self-adjust automatically without external intervention or time-consuming learning processes
3Manufacturing precision
If the sample liquid flow becomes thicker due to insufficient or uneven sheath liquid flow, then the imaging region is compromised, but maintaining proper flow control adds system complexity
Solution Approach 1:
The invention implements a feedback mechanism where the system checks the thickness of the sample liquid flow by analyzing the distribution of density-based feature parameter values. When the flow thickness deviates from the optimal range, the system can issue warnings or adjust operations, maintaining imaging quality without requiring complex real-time flow control mechanisms
Data Source
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AI summary
The in-focus point is to be adjusted accurately and efficiently, and also displacement of the in-focus position and the thickness of the flow of a sample liquid are to be checked accurately. At each of mutually different multiple focal positions, focal adjustment parameter values are obtained from images of standard particles made of the same substance. Each focal adjustment parameter value is figured out as any one of the ratio between the density value around the center of the standard particle image and the density value around the outline, the difference therebetween, and the density value around the center. The in-focus position is adjusted on the basis of the relationship between the obtained focal adjustment parameter values and the focal positions. Moreover, on the basis of the relationship between the focal adjustment parameter values and the focal positions, the parameter values are converted into focal positions, and the focal positions and dispersion thereof are used to check the displacement of the in-focus position and the thickness of the sample liquid.