Adaptive Threshold Imaging for Blood Sample Extraction
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Solution Overview
Problem
Existing methods for recognizing sample areas on sample cards with non-uniformly distributed blood spots, particularly those with varying haemoglobin levels, result in a high percentage of usable samples being disregarded due to inconsistent darkness, leading to the need for more blood samples from donors.
Innovation Solution
Determining individual threshold values for each sample spot and calculating punching regions based on intensity values to accurately identify and extract usable sample areas, allowing for customizable punching coordinates that adapt to varying shapes and sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed threshold value is used for all sample spots, then the apparatus is simple to operate, but many usable samples are incorrectly rejected due to varying blood distribution and haemoglobin levels
Solution Approach 1:
The patent applies local quality by determining individual threshold values for each sample spot based on its specific characteristics. Instead of using a uniform threshold across all spots, the system analyzes each spot's intensity distribution and establishes a localized threshold that adapts to variations in blood distribution, spot size, and haemoglobin concentration. This resolves the contradiction by maintaining operational simplicity while significantly improving recognition reliability for diverse sample conditions.
Solution Approach 2:
The patent implements dynamics by making the threshold value adaptive rather than static. The threshold determination process dynamically adjusts based on the actual intensity values observed in each sample spot, allowing the system to respond to varying sample conditions. This dynamic approach enables the apparatus to handle arbitrary blood distributions and varying haemoglobin levels without requiring complex manual calibration for each sample type.
2Productivity
If a fixed threshold value is used for all sample spots, then the processing is fast and simple, but the precision of sample region identification deteriorates
Solution Approach 1:
The patent applies local quality by determining individual threshold values for each sample spot based on its specific characteristics. Instead of using a uniform threshold across all spots, the system analyzes each spot's intensity distribution and establishes a localized threshold that adapts to variations in blood distribution, spot size, and haemoglobin concentration. This resolves the contradiction by maintaining operational simplicity while significantly improving recognition reliability for diverse sample conditions.
Solution Approach 2:
The patent changes the parameter of threshold values from fixed to variable, where each sample spot receives a customized threshold based on its intensity characteristics. This parameter adaptation allows the system to maintain high processing speed while achieving accurate identification of sample regions even when blood distribution and concentration vary significantly across different spots.
3Measurement precision
If individual threshold values are determined for each sample spot, then the recognition accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by performing threshold determination as an integrated part of the automated imaging and analysis process. The system captures the image, immediately processes the intensity values to determine appropriate thresholds, and proceeds to identification without requiring separate manual calibration steps. This preliminary automated thresholding maintains high precision while minimizing additional processing time compared to manual methods.
4Reliability
If individual threshold values are determined for each sample spot, then more usable samples are recognized, but the device complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine its own threshold values without requiring external calibration or manual intervention. The apparatus processes the image data, identifies intensity characteristics of each sample spot, and autonomously establishes appropriate thresholds for recognition. This self-determining approach improves reliability for diverse samples while avoiding the complexity of manual calibration procedures or additional calibration hardware.
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
This approach increases the reliability and efficiency of sample disc extraction, reducing the number of samples required and improving donor comfort by ensuring more accurate and efficient analysis.
Implementation Method 1
optically imaging the at least one impregnated sample in order to form an image
Data Source
Figure 1~2
AI summary
Method and apparatus for determining from a sample carrier containing an impregnated biological sample a sample region to be removed. The method comprises optically imaging the sample carrier into a digital matrix form, where the optical brightness of each physical location of the sample is represented by elements of the matrix; determining a first threshold value at least partly on the basis of the elements of the matrix; and calculating coordinates corresponding to the sample region to be removed from the sample carrier, the coordinates being such that the sample region contains only areas of the sample carrier having an optical brightness lower than or equal to said first threshold value which allows for more efficient use of samples in sample cards.