Precise particle tracking against a complicated background: polynomial fitting with Gaussian weight

Abstract

We present a new particle tracking software algorithm designed to accurately track the motion of lowcontrast particles against a background with large variations in light levels. The method is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre, and is especially suitable for tracking endogeneous particles in the cell, imaged with bright field, phase contrast or fluorescence optical microscopy. Furthermore, the method can simultaneously track particles of all different sizes, and allows significant freedom in their shape. The algorithm is evaluated using the quantitative measures of accuracy and precision of previous authors, using simulated images at variable signal-to-noise ratios. To these we add a new test of the error due to a non-uniform background. Finally the tracking of particles in real cell images is demonstrated. The method is made freely available for non-commencial use as a software package with a graphical user-inferface, which can be run within the Matlab programming environment.

An exploration of the microrheological environment around the distal ileal villi and proximal colonic mucosa of the possum ( Trichosurus vulpecula )

“…In each frame, x -y coordinate data were extracted for the ensemble of microbeads using a Gaussian weighted polynomial fit to the image intensity [30]. It was not possible to survey Brownian motion directly in three dimensions as such a procedure would need to rely on consistent changes in the appearance of microbeads along the z-axis as a function of their distance from the focal plane of the microscope.…”

A Practical Review of Microrheological Techniques

“…This powerful tracking algorithm has a good graphical user interface and is easy to implement. Details of the algorithm can be viewed in the following publication [26].…”

Kinetics of the melting front in two-dimensional plasma crystals: Complementary analysis with the particle image and particle tracking velocimetries

“…A summary of these results are presented in Table I. The particle tracking was performed using the PolyParticleTracker software by Rogers et al [21]. The tracking of the dust particles followed these steps: First the image was smoothed in order to reduce the noise, then the particles were identified in order to obtain a first estimate of the particles coordinates followed by a local fit at each feature with a quadratic surface in order to have a sub-pixel refinement of the particle coordinates.…”

“…Then real particles are dissociated from false track and finally the particle position are linked between frames. A detailed description of the software is given by Rogers et al [21].…”