Toolbox for tracking and analyzing crowded mixture of colloidal particles

Abstract

We present a MATLAB-based multiple particle tracking (mPosTracker) algorithm to detect and track crowded colloidal particles that are two-dimensionally imaged using digital video microscopy. Our algorithm adopts three optional detection methods using threshold settings namely; centroid method (CM), radial symmetry method (RSM) and partial radial symmetry method (pRSM). In contrast to other reported algorithms, it allows classifying a maximum of three types of particles based on area or intensity. Furthermore, we include a toolbox to perform quantitative particle analysis like number density, trajectories with mean square displacement, and Voronoi cells with six-fold bond parameters to reveal the physics of examined dataset. To demonstrate the abilities of mPosTracker, we show the analysis outcomes on synthetically and experimentally acquired time-lapse microscopy datasets. mPosTracker is suitable for studying the dynamics of crowd behaviour, self-assembly, and manipulation of multi-component colloids under external fields.