Tube Cap Image Classification for Accurate Tube Type Identification
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Solution Overview
Problem
Automated testing systems face challenges in accurately identifying tube assembly types due to variations in cap colors and manufacturer-specific standards, leading to improper characterization and potential errors in clinical chemistry assays.
Innovation Solution
A method and apparatus that utilize machine learning techniques, including discriminative models and image processing, to analyze cap geometry, color, and other characteristics of tube assemblies, enabling accurate identification of tube types by capturing and analyzing pixilated images with front and back illumination, and converting color values to HSV space for input into a linear discriminant analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different types of dialysisators are used for different indications, then treatment versatility is improved, but device complexity and inventory management become problematic
Solution Approach 1:
The patent applies universality by designing a single tube assembly that can be used across multiple dialysisator types and indications. The standardized tube assembly with specific dimensions, materials, and connection features enables it to function in different dialysis treatments (hemodialysis, peritoneal dialysis, etc.) without requiring separate specialized components for each indication, thereby reducing device complexity while maintaining treatment versatility.
Solution Approach 2:
The patent applies segmentation by separating the tube assembly into distinct, modular components with standardized interfaces. The tube assembly is designed as an independent module that can be universally integrated into different dialysisator systems, allowing the system to be segmented into interchangeable components rather than requiring complete system-specific assemblies for each indication.
2Ease of manufacture
If tube assembly specifications are not clearly identified, then manufacturing flexibility is maintained, but clinical safety and proper usage are compromised
Solution Approach 1:
The patent applies visual identification methods including color coding and fluorescent markers on the tube assembly. These visual features provide immediate, unambiguous identification of tube assembly type and specifications without affecting manufacturing flexibility. The color and fluorescent characteristics are incorporated into the tube material or coating during manufacturing, allowing easy visual verification in clinical settings to ensure proper tube assembly selection and usage.
Solution Approach 2:
The patent applies self-service by incorporating self-identifying features directly on the tube assembly that allow healthcare providers to independently verify tube assembly specifications without requiring external documentation or complex identification systems. The visual markers enable the tube assembly to 'identify itself' regarding its type and appropriate applications, ensuring clinical safety while maintaining manufacturing simplicity.
3Measurement precision
If visual identification features are added to tube assemblies, then identification accuracy is improved, but manufacturing complexity increases
Solution Approach 1:
The patent applies color coding and fluorescent marking techniques that can be integrated into existing manufacturing processes. These visual identification features are applied during tube production through standard extrusion or coating methods, adding minimal complexity to manufacturing while significantly improving identification accuracy. The color and fluorescent characteristics become inherent properties of the tube material rather than separate complex components.
Data Source
Figure 1A~2B
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Figure 5A~5D
AI summary
A method of identifying a tube type. The method includes capturing one or more pixelated images of a cap affixed to a tube; identifying a color of one or more pixels of the pixilated image of the cap; identifying one or more gradients of a dimension of the cap; and identifying the tube type based at least on: the color of the one or more pixels, and the one or more gradients of a dimension of the cap. Apparatus adapted to carry out the method are disclosed as are other aspects.