Toolbox for tracking and analyzing crowded mixture of colloidal particles
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Digital video microscopy (DVM), Image processing, Particle detection, Particle tracking, Particle classification, Binary colloids, Crowded particles
Kaynak
Colloid and Interface Science Communications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
45
