Parameters

The MicrobeTracker grabs the parameters from the window at the beginning of any operation. Any changes made afterwards will have no effect. Not all of the parameters are used for every operation, the rest are just ignored. Some of the parameters sets are saved as default, but they may need to be modified depending on the particular image set. Usually only a few parameters have to be changed, these parameters are written in bold and their effect described in more detail on the Adjusting Parameters page. Most of the parameters are numeric. The logical parameters (yes/no) are also numeric, with yes=1 and no=0.

Algorithm

algorithm is the most important parameter, which determines how the cells are outlined. For a more description of the difference between algorithms see Cell outlining algorithms section. If you are detecting cells in a timelapse series, the algorithm used must be the same, the program will not be able to continue otherwise. Currently, 4 algorithms are implemented (numbered 1 to 4), though their quality of work and intended use differ.

Algorithm 1 is the most primitive and the fastest algorithm based exclusively on morphological operations, such as thresholding, edge detection, and watershed algorithms. It is the closest to what most previous object or cell detection program offered. This algorithm should be used when nothing is known about the cell shape. However, if it tries to create a mesh (which could be disabled with getmesh parameter), it will not detect a cell if this is impossible for a particular object. This algorithm uses a minimum number of parameters, only such parameters as fsmooth, areaMin, areaMax, fmeshstep, maxmesh, interpoutline (and related), and valley/edge parameters will affect detection.

The algorithms 2-4 use an attraction map, attracting the cell outline contour to particular points on the image, which is opposed by resistance related to internal shape constraints. The nature of these constraints is different for each algorithm and is described below.

Algorithm 2 uses the same procedure as algorithm 1 for initial guess, which is then refined using the Point Distribution Model (PDM). In timelapses, the outline produced for the previous frame is used as the initial guess. This algorithm is faster than the following algorithms and works only for the cells which it is trained for. Currently only the data for "normal" Caulobacter cells is included, but training is relatively simple and can be performed by the user (see subsection Training).

Algorithm 3 is a modification of algorithm 2, though is uses a further modified variant of the PDM to work on elongated cells. It currently requires additional attention to develop the best parameter set and solve some common issues.

Algorithm 4 is based on a version of the Active Contour Model (snake). It is the slowest method and requires setting all parameters manually. It was developed for filamentous cells, but is works well for all cell types. It is recommended for the applications with filamentous cells and non-Caulobacter rod-shaped cells. It will not work well for noticeably non-rod-shaped cells.

List of parameters

The most important parameters that should be changed relatively frequently are indicated in bold. The typical values are shown for wild type Caulobacter crescentus cells imaged in phase contrast regime at 0.064 μm/pixel resolution, unless mentioned otherwise.

General

Algorithm 1 specific

Pixel-based operations

PDM model specific (for algorithms 2 and 3)

Constraints (for algorithm 4)

Image forces (for algorithms 2-4)

Contour fitting (for algorithms 2-4)

Mesh creation

Joining and splitting

Other

 

See also: Adjusting Parameters. Top pages: MicrobeTracker, MicrobeTracker Suite.