Object tracking

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

Video protocol: https://www.youtube.com/watch?v=oC9tp5TKAyw

Example image: Example video of 5 zebrafish

The website implements a set of computer vision algorithms designed to automatically process time-lapse images of fluorescently labeled focal adhesion proteins in motile cells.

The methods associated with the processing have been published in PLOS One and Cell. The publication describes a quantitative analysis of focal adhesion dynamics that have been imaged using TIRF. All image processing steps are well explained or referenced.

To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones.

u-Track is a client-side OMERO MATLAB application implementing the sophisticated multiple-particle tracking algorithm of Jaqaman et al. . It works on data previously imported into an OMERO server, and produces results in the form of MATLAB data structures as well as providing functionality to visualise these results.

This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.

Documentation starts from p23 of the linked PDF. 

Example Image: mitocheck_small.tif (2.9M)