Songyuan-Henry-Yan/Optimized-Hough-Circle-Transform-for-Fast-and-Accurate-Microparticle-Detection-and-Counting: v3.0 - Optimized HCT Workflow for Fast Microparticle Counting

v3.0 - Optimized HCT Workflow for Fast Microparticle Counting This release provides the revised Hough Circle Transform (HCT)-based workflow for microparticle detection and counting in microfluidic image sequences. Major updates Updated repository title and documentation to reflect the revised HCT-focused workflow. Replaced outdated scripts with four revised code files: HoughGUI_1size_saveScreenshots.py HoughGUI_2size_saveScreenshots.py HCT_optimizer_latest_optimized_version.py HCT_optimizer_manuscript_publication.py Added GUI tools for one-size and two-size bead detection workflows. Added an expanded optimizer version with duplicate-circle removal, ranked parameter outputs, annotated preview images, best-parameter summary files, and range-edge warnings. Added a cleaned manuscript/publication optimizer version. Expanded README user instructions for image preparation, manual count input, parameter range selection, optimizer execution, output interpretation, and error-rate calculation. Updated the workflow for 3 µm and 5 µm microparticle image analysis. Recommended use Use the GUI scripts to visually estimate HCT parameters, then run the optimizer scripts using manually counted training images. The selected parameter set can then be applied to validation images for final counting-error analysis. This release corresponds to the revised manuscript software workflow.