Mask Structure Optimization for Fine Particle Classification
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Solution Overview
Problem
Existing technologies face challenges in improving the classification accuracy of fine particles, including cells and bacteria, based on morphological information.
Innovation Solution
A mask structure optimization device and method that includes a classification target image size acquisition unit, mask size setting unit, brightness detection unit, sum total brightness calculation unit, initial value setting unit, and movement unit, which optimize the mask pattern using a binary convolutional neural network to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mask pattern is generated using Bernoulli distribution, then the mask structure is simple to generate, but the classification accuracy of fine particles is insufficient
Solution Approach 1:
The patent performs preliminary optimization of the mask pattern before actual classification. The optimization unit calculates sum total brightness values for multiple candidate mask patterns and selects the optimal pattern in advance, ensuring high classification accuracy without increasing runtime complexity.
Solution Approach 2:
The patent replaces the random Bernoulli distribution mechanism with a systematic optimization mechanism based on sum total brightness calculation. This substitution transforms the mask generation from a random process to an optimized process that maximizes classification performance.
2Measurement precision
If the mask size is increased to improve classification accuracy, then more particles can be captured, but the processing time increases
Solution Approach 1:
The patent optimizes the mask pattern parameters (transmittance values at different positions) to achieve high classification accuracy with smaller mask sizes. By adjusting the sum total brightness parameters, the system achieves better performance without increasing mask dimensions or processing time.
Data Source
AI summary
A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image. The sum total brightness calculation unit is configured to calculate the sum total brightness of the each pixel within the classification target image every time the movement unit relatively moves the mask by a predetermined movement amount. The mask structure optimization device further includes a mask pattern optimization unit that is configured to optimize the mask pattern of the mask on the basis of the sum total brightness.