Multi-Layer Focus Quality Detection for Blood Cell Imaging
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Solution Overview
Problem
Existing blood cell analysis systems face challenges in capturing high-quality images due to focus issues caused by temperature changes and ineffective focusing methods for different types of blood cells, necessitating improvements in autofocusing and refocusing during image acquisition.
Innovation Solution
A system utilizing a camera, convolution filters, and a fully connected layer to automatically determine focusing quality by convolving images with multiple filter sets and adjusting the focus distance based on neural network outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional focusing methods are used, then the system can capture images of some blood cell types, but the focusing quality deteriorates when temperature changes or when analyzing different types of blood cells
Solution Approach 1:
The system performs self-diagnosis by analyzing captured images to detect focus quality issues. The processor automatically evaluates the sharpness and clarity of blood cell images, identifies when focusing has deteriorated due to temperature changes or other factors, and triggers refocusing operations without external intervention, enabling the system to maintain optimal focusing quality autonomously
Solution Approach 2:
The system implements a feedback loop where image quality assessment results are fed back to control the refocusing mechanism. The processor continuously monitors focusing quality metrics from captured images and adjusts the imaging system's focus position accordingly, creating a closed-loop control system that adapts to changing conditions and maintains measurement precision
2Measurement precision
If manual refocusing is performed, then focus quality can be maintained, but the image acquisition process becomes time-consuming and reduces productivity
Solution Approach 1:
The system automatically detects focus deterioration and executes refocusing operations without requiring manual intervention. The processor monitors image quality in real-time, identifies when refocusing is needed, and controls the imaging system to adjust focus, thereby maintaining high image quality while eliminating the time loss associated with manual refocusing operations and preserving productivity
Solution Approach 2:
The system performs preliminary focus assessment on captured images before they are used for analysis. By evaluating focus quality early in the workflow and proactively triggering refocusing when needed, the system prevents degradation of image quality and avoids the need for re-capturing images, thus maintaining both high productivity and measurement precision
3Adaptability or versatility
If feature extraction-based focusing is used, then red blood cells can be focused effectively, but the method fails to provide adequate focusing for white blood cells and other cell types
Solution Approach 1:
The system employs a universal focus assessment mechanism that evaluates image quality based on general optical principles applicable to all blood cell types. Rather than relying on cell-type-specific features, the system uses metrics such as overall image sharpness, edge definition, and contrast that are effective for red blood cells, white blood cells, and other cellular elements, thereby achieving adaptable and accurate focusing across diverse cell types
Data Source
AI summary
A focusing distance (e.g., a distance between a focal plane of a camera used when n image was captured and the actual focal plane for an in-focus image) in a visual analysis system may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, an input may be subjected to one or more convolution filters, and the ultimate result of such convolution may be provided to a dense layer which can provide the focusing distance. This focusing distance may then be used to (re) focus a camera or for other purposes (e.g., generating an alert).