Specimen Container Identification Using Multi-Spectral Imaging
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Solution Overview
Problem
Existing methods struggle to accurately determine the size and type of specimen containers in automated testing systems without visual inspection, leading to potential human error and inefficiencies, especially when containers are labeled or contain varying sizes and materials.
Innovation Solution
A method and apparatus that capture images of specimen containers at different exposure times and spectra, using multiple cameras to select optimally-exposed pixels and classify them as tube, cap, or label, to identify width, height, or both, enabling automated determination of container characteristics without rotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras capture images at different exposure times and spectra, then measurement precision of container dimensions is improved, but device complexity increases
Solution Approach 1:
The imaging system is segmented into multiple cameras, each capturing images at different exposure times and spectra. This segmentation allows simultaneous capture of multiple image parameters without requiring sequential imaging, thereby improving measurement precision while managing system complexity through parallel processing architecture
Solution Approach 2:
The system transitions from single-dimension imaging to multi-dimensional imaging by capturing images across multiple exposure times and spectral dimensions simultaneously. This dimensional expansion enables comprehensive container characterization including transparent, translucent, and opaque portions through holistic image data fusion
2Productivity
If automated image processing is used to identify container characteristics, then productivity is improved, but measurement precision may worsen due to label occlusion
Solution Approach 1:
The system applies local quality processing by treating different regions of the container differently based on their optical properties. Transparent portions are analyzed using transmission imaging, translucent portions using reflection imaging, and opaque portions using alternative imaging modes, with each region processed according to its specific characteristics to maintain precision while enabling automation
Solution Approach 2:
The system overcomes label occlusion by transitioning to multi-dimensional imaging across multiple exposure times and spectra. This dimensional expansion provides alternative viewing angles and spectral information that penetrate or bypass label obstructions, maintaining measurement precision while enabling fully automated processing
3Device complexity
If single exposure time imaging is used, then device complexity is reduced, but loss of information increases for containers with varying transparency
Solution Approach 1:
The imaging system segments the container into different transparency-based regions (transparent, translucent, opaque) and captures each region optimally using dedicated exposure times and spectra. This segmentation ensures that no portion of the container loses critical visibility information, with each region imaged under conditions optimized for its specific optical properties
Solution Approach 2:
The system changes imaging parameters (exposure time and spectra) based on the optical properties of different container portions. By dynamically adjusting these parameters across multiple imaging captures, the system preserves complete information about all container portions including those with varying transparency, preventing information loss that would occur with single-parameter imaging
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 allows for precise and automated identification of specimen container dimensions and types, reducing human error and maintaining the speed of analytical testing processes, even on labeled containers, while ensuring compatibility with various additives and test types.
Implementation Method 1
capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths
Implementation Method 2
capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths
Data Source
AI summary
A model-based method of determining characteristics of a specimen container. The method includes providing a specimen container, capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths, selecting optimally-exposed pixels from the images at different exposure times at each spectra to generate optimally-exposed image data for each spectra, and classifying the optimally-exposed pixels as at least being one of tube, label or cap, and identifying a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.