Blood Cell Imaging Chamber Analysis Using Pixel-Based Counting
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Solution Overview
Problem
Existing blood cell analysis methods, such as those using hemocytometers and flow cytometers, are labor-intensive, prone to errors, and costly, and face challenges in accurately classifying, counting, and calculating cell concentrations due to the need for precise chip dimensions and complex optical systems.
Innovation Solution
A blood imaging analysis system and method that uses a simple, low-cost chip with a camera module, focusing module, and digital image processing to obtain clear digital images of blood cells, allowing for accurate classification, identification, and concentration calculation through pixel counting and image analysis without the need for precise grids on the chip.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting using a hemocytometer is used, then cell concentration can be measured, but the process is time-consuming, labor-intensive, and prone to errors
Solution Approach 1:
The patent replaces manual mechanical counting with an automated digital imaging system. A camera module captures images of blood cells in a chamber, and a processor automatically counts and classifies cells based on image data, eliminating manual intervention and reducing time consumption while maintaining measurement accuracy
Solution Approach 2:
The patent creates a digital copy (image) of the blood cell sample instead of physically counting individual cells. The camera module captures optical images of cells in the chamber, and the processor analyzes these digital representations to determine cell concentration, replacing the physical counting process
2Measurement precision
If manual counting using a hemocytometer is used, then cell concentration can be measured, but the process is labor-intensive
Solution Approach 1:
The patent replaces manual mechanical counting operations with an automated digital imaging and processing system. The camera module automatically captures images, and the processor performs cell classification and concentration calculation without human intervention, significantly reducing labor requirements
Solution Approach 2:
The system performs self-service by automatically acquiring images, processing data, classifying cells, and calculating concentrations without requiring skilled operators. The processor handles all analytical tasks independently once the sample is loaded into the chamber
3Measurement precision
If standard-sized grids are used on the hemocytometer, then cell concentration can be calculated, but high manufacturing accuracy is required resulting in high implementation costs
Solution Approach 1:
The patent replaces physical standard grids with digital image processing. Instead of requiring precisely manufactured grid lines on the chamber, the system captures images and uses software algorithms to define measurement areas and calculate concentrations, eliminating the need for high-precision manufacturing
Solution Approach 2:
The patent substitutes mechanical grid structures with digital measurement systems. The camera module captures images, and the processor uses software-based reference frames and algorithms to perform measurements, replacing the need for physically precise grids with computational accuracy
4Measurement precision
If flow cytometers are used for cell analysis, then cell concentration and classification can be achieved, but the system is complex and costly
Solution Approach 1:
The patent extracts the essential function of cell analysis from complex flow cytometry systems. Instead of using sophisticated flow cytometers with multiple lasers and detectors, the system uses a simple camera module to capture images and a processor to perform analysis, retaining the core functionality while eliminating unnecessary complexity
Solution Approach 2:
The patent employs inexpensive disposable imaging chambers instead of expensive, complex flow cytometers. The chamber is a simple transparent container that can be easily manufactured and discarded, replacing costly, maintenance-intensive flow cytometry equipment
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
Enables precise cell classification, identification, and concentration calculation with reduced costs and errors by determining the actual size of the imaged area and volume of the blood sample mixture using fixed magnification and pixel area parameters, enhancing accuracy and efficiency.
Implementation Method 1
a camera module, a focusing module and a digital image processing module... the camera module is configured to obtain the clear digital image
Data Source
AI summary
A blood imaging analysis system includes a chip and an imaging analysis main system. The height of the chamber containing the test liquid in the chip is H; the area magnification factor of the camera module is K; the imaged area of the test area (S1) in the camera component is S2, and the unit pixel area is SK. A clear image including various blood cells or particles is obtained by satisfying S2=S1×K. The pixel count of S2 is M=S2/SK. The imaged area (S2) is obtained by calculating the pixel count (M). The image matching analysis and processing of the blood cells or particles in the imaged area (S2) is carried out to achieve classification recognition and counting, and the count (CN) of different types of blood cells or particles in the volume (S1×H) is obtained.