An automated test tube identification and sorting system based on machine vision

This research presents a machine vision system for automated test tube identification and sorting in medical laboratories, improving upon manual methods that are error-prone and inefficient. The system employs digital image processing to detect specimen presence via tube caps, enhancing accuracy and throughput. The setup includes a transparent test tube rack with CCD cameras capturing images against a black background. Images are processed from RGB to HSI format for better color differentiation and smoothed to reduce noise. Edge and circle detection algorithms pinpoint the locations of test tubes for robotic arm sorting. Experiments across various filling scenarios validate the system's effectiveness. The system promises to reduce human error, optimize lab workflows, and increase automation in healthcare diagnostics.