Specimen Artifact Detection via Multi-Spectral HDR Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated testing systems face challenges in accurately detecting artifacts like clots, bubbles, and foam in biological specimens, which can lead to inaccurate test results and require manual intervention, especially when specimen labels obstruct the view.
Innovation Solution
A method and apparatus using high dynamic range (HDR) image processing and multiple cameras to capture images of specimens at different exposure times and wavelengths, selecting optimally exposed pixels, computing statistics, and classifying the presence of artifacts in the serum or plasma portion, allowing for automated detection without manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection by skilled laboratory technician is used to determine specimen integrity, then detection accuracy can be maintained through human expertise, but labor intensity increases and human error becomes a significant risk
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated optical imaging and analysis system. Multiple cameras capture images of the specimen container, and a computer automatically processes these images to detect artifacts such as clots, bubbles, and foam. This substitution eliminates human labor requirements while maintaining detection accuracy through systematic image processing algorithms.
Solution Approach 2:
The patent creates visual copies of the specimen through multiple camera images captured at different exposures and wavelengths. These image copies allow automated analysis without requiring direct human visual inspection, enabling the system to examine specimen integrity through digital representations rather than manual observation.
2Productivity
If bar code labels are adhered to specimen containers for identification and tracking, then specimen management efficiency is improved, but the labels may partially occlude the view of the specimen and interfere with automated inspection
Solution Approach 1:
The patent segments the inspection process by capturing images at multiple different exposures and wavelengths. This segmentation allows the system to process different aspects of the specimen separately - some images optimized for label reading, others optimized for specimen visualization - and combine the information to achieve both specimen management and artifact detection goals.
Solution Approach 2:
The patent adds spectral dimensionality to the inspection process by capturing images at multiple wavelengths. This allows the system to exploit differences in how labels and specimen components absorb or transmit light at different wavelengths, enabling the computer to distinguish between label material and specimen features even when they occupy the same spatial region.
3Productivity
If automated pre-inspection methods are implemented to evaluate specimen integrity, then labor requirements are reduced and productivity increases, but the complexity of the detection system increases
Solution Approach 1:
The patent implements a multi-functional inspection system where the same imaging apparatus serves multiple purposes: capturing images for artifact detection, obtaining data for specimen characterization, and supporting both labeled and unlabeled container types. The computer executes multiple analysis functions using the same captured images, reducing the need for separate specialized devices.
Solution Approach 2:
The patent merges multiple inspection functions into a single integrated system. The same set of cameras and imaging apparatus that capture images for artifact detection also provides data for specimen identification and characterization. The computer combines multiple analysis tasks - artifact classification, specimen typing, and quality assessment - into a unified processing workflow.
Data Source
Figure 1~2
Figure 3~4
Figure 5A~5B
AI summary
A model-based method of inspecting a specimen for presence of one or more artifacts (e.g., a clot, bubble, and/or foam). The method includes capturing multiple images of the specimen at multiple different exposures and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, computing statistics of the optimally-exposed pixels to generate statistical data, identifying a serum or plasma portion of the specimen, and classifying, based on the statistical data, whether an artifact is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.