In order to validate our choice of open-source tracking software, a synthetic video based on synthetic data was created. This video was then fed into the software for cell tracking, afterwhich data analysis to obtain bacteria motility characteristics was carried out. The data output was then compared with the synthetic data generated so as to ensure the effectiveness and integrity of the tracking software. The following figure describes our approach in validating the tracking software.
Bacteria light intensity or color, shape, size and orientation are being generated in a single m-file. A function leading to the generation of these parameters is then called by the main function which generates synthetic video.
Distributions of bacteria run velocity, run duration, tumbling angle and tumbling duration were generated using alternative models with arbitrary parameters assumed. Frame-by-frame coordinates are then returned to the main function, allowing the trajectory of bacteria to be plotted.
A synthetic video of user defined number of bacteria was created using MATLAB. The function calls motility data and bacteria characteristic generating functions, and with these, it plots the trajectory of "motile" bacteria. The background image on which the video is generated also changes with time, introducing an element of noise.
The tracking software used will receive the generated synthetic video as an input, and commence cell tracking on a frame-by-frame basis. Bacteria trajectory will then be produced as the data output.
Bacteria trajectory data in terms of coordinates are analysed and compared with synthetic data. The errors associated with the resective tracking software are determined via this comparison. Other factors such as the ease of use of software and amount of time used to track cells are also taken into consideration when evaluating the software.
Bacteria motility characteristics such as run velocity, run duration, tumbling angle and tumbling duration will be generated by using algorithms applied to the trajectory data produced by the tracking software. The parameters will then be reconstructed using available alternative models.