Optimized Hough Circle Transform for Automated Microparticle Counting in Microfluidic Platforms

Area of Science:

• Microfluidics
• Particle Characterization
• Image Analysis

Background:

• Accurate particle identification and counting are crucial for microfluidic analysis and electrokinetic studies.
• Current methods for characterizing microfabricated systems often require manual or less efficient automated processes.

Purpose of the Study:

• To develop and optimize a Hough Circle Transform (HCT) workflow for automated particle detection, sizing, and counting.
• To enhance the efficiency and accuracy of particle enumeration in microfluidic applications.

Main Methods:

• Fabrication of gold interdigitated electrode arrays (IDEAs) to generate electroosmotic flow.
• Utilized 3 μm and 5 μm polystyrene microbeads as model particles.
• Implemented parallelized multicore parameter optimization and composite statistical metrics for HCT workflow.

Main Results:

• Achieved mean validation success rates of 85.1% (3 μm beads) and 90.0% (5 μm beads).
• Reduced parameter-selection time from 24-48 hours to 1-2 hours.
• Runtime image processing averaged 45 ms per frame, demonstrating significant time savings.

Conclusions:

• Parameter optimization is essential for robust HCT-based particle enumeration.
• The optimized workflow provides a practical and efficient analytical tool for microfluidic device characterization.
• The study validates the effectiveness of the HCT workflow for microparticle analysis in electrokinetic experiments.