Fluidic Substance Evaluation via Image Histogram Analysis
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Solution Overview
Problem
Current methods for evaluating fluidic substances, such as bodily fluids, in containers lack reliability, precision, and throughput, particularly in determining interferent concentrations and fluid volumes using dispense tips and image analysis.
Innovation Solution
A method involving image capture and processing to generate color parameters from images of fluidic substances, using histograms, mean values, Riemann sums, modes, maximums, minimums, and histogram percentages, to classify substances and determine interferent concentrations, along with reference point identification and volume measurement based on correlation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image capture and color parameter analysis methods are used to evaluate fluidic substances, then measurement precision of interferent concentration is improved, but device complexity increases
Solution Approach 1:
The image processing system is segmented into distinct functional modules: image capture device, histogram generation module, color parameter extraction module, and classification result generation module. Each module performs a specific function, allowing the complex analysis to be broken down into manageable components that can be developed, tested, and maintained independently while achieving high measurement precision for interferent concentration.
Solution Approach 2:
Histograms serve as an intermediary data structure between the raw image data and the final interferent concentration classification. The system generates histograms from image pixels, extracts color parameters from these histograms, and then uses these parameters to determine interferent concentrations. This intermediary approach simplifies the overall processing by transforming complex image data into manageable statistical representations.
2Reliability
If multiple color parameters are extracted from images, then reliability of substance evaluation is improved, but loss of time in processing increases
Solution Approach 1:
The system performs preliminary actions by generating histograms and extracting multiple color parameters (mean values, Riemann sums, modes, maximums, minimums, histogram percentages) from the image data in advance. These pre-computed parameters are then used directly for interferent concentration classification, avoiding the need for repeated complex calculations during the actual evaluation process and thereby reducing processing time while maintaining high reliability.
3Productivity
If automated image analysis is implemented for volume measurement, then productivity of substance evaluation is improved, but measurement precision of fluid volume may worsen
Solution Approach 1:
The system uses parameter changes by analyzing multiple color parameters from histograms rather than relying on a single metric. By examining mean values, Riemann sums, modes, maximums, minimums, and histogram percentages across different color channels, the automated analysis achieves both high productivity through automation and maintains measurement precision by using multiple complementary parameters to cross-validate the volume measurements.
Data Source
AI summary
Automatic substance preparation and evaluation systems and methods are provided for preparing and evaluating a fluidic substance, such as e.g. a sample with bodily fluid, in a container and/or in a dispense tip. The systems and methods can detect volumes, evaluate integrities, and check particle concentrations in the container and/or the dispense tip.