Barcode Tag Condition Classification for Sample Tube Automation
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Solution Overview
Problem
Deterioration of barcode tags on sample tubes, such as tearing, peeling, and discoloring, hinders the efficient handling of sample tubes in clinical laboratory automation systems, as existing systems lack an automatic and efficient method for classifying and addressing these issues.
Innovation Solution
A vision system captures top-view images of sample tubes to classify barcode tag conditions using region-of-interest extraction, rectification, and feature extraction, grouping conditions into categories like 'good', 'warning', and 'error', with subcategories for deformations, and employs pixel-based classification for localized problematic area identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If barcode tags are used on sample tubes for identification and tracking, then sample tube identification and tracking are enabled, but barcode tag deterioration (tearing, peeling, discoloring) occurs during normal use which hinders automated processing
Solution Approach 1:
The vision system performs preliminary detection and classification of barcode tag conditions before they completely deteriorate. By capturing images and analyzing barcode tag status proactively during sample tube processing, the system can identify deteriorating tags early and take corrective actions such as alerting operators or rerouting samples, preventing complete identification failure.
2Measurement precision
If manual inspection of barcode tags is performed to detect deterioration, then problematic tags can be identified, but processing efficiency is reduced and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated vision system that uses image capture and computer algorithms to detect and classify barcode tag conditions. The system captures images of sample tubes, extracts features from the images, and automatically classifies barcode tag status, eliminating the need for manual inspection while maintaining high detection accuracy and processing efficiency.
3Ease of operation
If existing systems process all sample tubes through the same workflow, then processing is simplified, but deteriorated barcode tags cause processing failures and require manual intervention
Solution Approach 1:
The vision system segments the processing workflow by classifying sample tubes into different categories based on barcode tag condition (e.g., good, warning, error). This segmentation enables differentiated handling: tubes with good barcode tags proceed through the normal automated workflow, while tubes with deteriorated tags are routed to alternative processes such as operator review or relabeling, maintaining overall system simplicity while improving processing success rate.
Data Source
AI summary
Embodiments are directed to classifying barcode tag conditions on sample tubes from top view images to streamline sample tube handling in advanced clinical laboratory automation systems. The classification of barcode tag conditions leads to the automatic detection of problematic barcode tags, allowing for a user to take necessary steps to fix the problematic barcode tags. A vision system is utilized to perform the automatic classification of barcode tag conditions on sample tubes from top view images. The classification of barcode tag conditions on sample tubes from top view images is based on the following factors: (1) a region-of-interest (ROI) extraction and rectification method based on sample tube detection; (2) a barcode tag condition classification method based on holistic features uniformly sampled from the rectified ROI; and (3) a problematic barcode tag area localization method based on pixel-based feature extraction.