Multi-Candidate Tube Top Circle Detection for IVD Sample Handling
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Solution Overview
Problem
Existing sample handling mechanisms in in vitro diagnostics (IVD) labs require manual interaction to identify tube characteristics, which is inefficient and prone to errors.
Innovation Solution
An image-based method using multi-candidate selection for tube top circle detection, which involves acquiring images of a tray with multiple tube slots, extracting candidates for each tube, computing consistency scores across images, and selecting the true tube top circle based on the highest consistency score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interaction is used to identify tube characteristics, then operator judgment can handle complex cases, but efficiency is reduced and errors increase
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical imaging system. Multiple images are captured from different angles and processed through circle detection algorithms to automatically identify tube characteristics, eliminating the need for manual operator intervention while maintaining or improving accuracy.
Solution Approach 2:
The system creates multiple digital copies (images) of the tube from different perspectives. These image copies are then analyzed through consistent circle detection methods, allowing the system to reconstruct accurate tube characteristics without physical manipulation of the sample tubes.
2Productivity
If single-candidate circle detection is used, then processing is faster, but robustness decreases in challenging cases
Solution Approach 1:
The detection process is segmented into multiple independent stages: image acquisition from multiple angles, individual circle candidate detection in each image, consistency scoring across images, and final selection. This segmentation allows the system to maintain processing efficiency while improving robustness through multi-view verification.
Solution Approach 2:
The system implements feedback through consistency scoring, where circle detection results from multiple images are evaluated against each other. Candidates that consistently appear across multiple views receive higher scores, providing feedback that validates detection accuracy and improves reliability in challenging cases.
3Reliability
If multiple candidates are extracted per image, then detection robustness improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary circle candidate extraction in each individual image before combining results. By pre-identifying all potential circle candidates in each view and then evaluating their consistency across images, the system reduces the search space and computational burden of the overall detection process.
Solution Approach 2:
The problem is extended from two-dimensional single-image analysis to three-dimensional multi-image analysis. By adding the temporal dimension of multiple images captured at different times/angles, the system improves detection robustness through consistency verification while managing complexity through efficient multi-view processing algorithms.
4Productivity
If automated image-based detection is implemented, then efficiency increases and manual handling is reduced, but measurement precision must be maintained
Solution Approach 1:
The circle detection system is designed to be universal, handling various tube types, orientations, and imaging conditions through a single consistent algorithmic approach. The multi-candidate, multi-image methodology provides a unified framework that maintains measurement precision across diverse scenarios while enabling automated high-throughput processing.
Data Source
AI summary
Embodiments provide a method of using image-based tube top circle detection based on multiple candidate selection to localize the tube top circle region in input images. According to embodiments provided herein, the multi-candidate selection enhances the robustness of tube circle detection by making use of multiple views of the same tube to improve the robustness of tube top circle detection. With multiple candidates extracted from images under different viewpoints of the same tube, the multi-candidate selection algorithm selects an optimal combination among the candidates and provides more precise measurement of tube characteristics. This information is invaluable in an IVD environment in which a sample handler is processing the tubes and moving the tubes to analyzers for testing and analysis.