This section describes how to select the parameter file and to adjust the most sensitive parameters for a particular experiment. For a detailed description of all parameters see the Parameters page. After you have adjusted the parameters and the result seems to be good, you should save the parameter set to use in the future with similar experiments. The set you used before can also be retrieved from a saved results (meshes) file (make sure in this case that you select .mat filter when loading the file). If saved in a separate file (.set), the parameters can be edited with any external text editor.

1. Algorithm

The typical choice is algorithm 4, sometimes 1, and more rarely 2, they are selected by setting algorithm=1, algorithm=2, etc. Try algorithm 4 first, unless it is clear a different one should be used. The algorithm 3 has practically no benefits over the other algorithms, and therefore has not been tested in the latest versions of the program. The choice of the algorithm will mostly depends on the type of the image and of the cells. See Cell outlining algorithms page for more details.

• For filamentous rod-shaped bacteria algorithm 4 is the only option.
• For extremely small (area < 20 px²) or irregularly shaped (not rod-shaped) cell algorithm 1 is the only option, you should also consider runnig it without generating the meshes (getmesh=0).
• For rod-shaped bacteria any algorithm will work.
• Algorithm 4 is the slowest and the best in working with curved cells.
• Algorithm 2 is faster, but fails on elongated cells and requires a training set. In the distribution of the suite such set is currently only available for C. crecsentus (these cells have pointed ends, wide septal regions, are curved).
• Algorithm 1 is the fastest as it does not use the active contour step at all. This algorithm has poorer performance in separating cells in clusters and in tracking them in timelapses.
• If you are using algorithm 1, consider using it with (getmesh=1) or without (getmesh=0) meshes. Generation of meshes takes additional time, but may fail for irregularly-shaped cells. A mesh is required to produce a coordinate system inside, but frequently only the outline of the cell is sufficient.

2. Default parameter sets

After you have decided what algorithm to use, load the corresponding predefined set. There are several general sets alg1.set, alg2.set, alg4.set tested for C. crescentus under the resolution ~ 0.06 µm/px. Some other sets were tested for other cell types, such as alg2ecoli.set and alg4ecoli.set for E. coli cells under the same resolution. For other cells just select one of the standard sets and modify some of the parameters if necessary.

3. Image segmentation

Image segmentation is performed in two steps: thresholding and edge detection:

• Thresholding is detecting the parts of the image above a certain threshold. The threshold is detected automatically, but the algorithm may fail if objects other than cells are present or if the background is non-uniform.
  The threshold is multiplied by thresFactorM parameter. Default: 1, very high values of the parameter (such that the threshold exceeds the maximum value for the image bitdepth) will cause an error. On even images with multiple cells no adjustment is necessary. Automatic threshold detection may fail on the images that are uneven, only partially illuminated, have too few cells, or have a lot of dust particles. You can see that by either keeping the cell-free areas of the image (the program will typically process and discard multiple regions with the area of about a few pixels each, in this case increase thresFactorM), or by not seeing many cells (decrease thresFactorM).
  An alternative to thresFactorM is threshminlevel parameter which defines the fraction of the brightest pixels (between 0 and 1) that are excluded from the set of values used to calculate the threshold. This parameter should be included and set in the range 0.05 to 0.1 to eliminate the effect of bright dust particles, glass chips, or any other objects that may confuse the automatic threshold detection algorithm.
• Edge/valley detection is detecting boundaries of cells (detailed description). You can choose between Laplacian of Gaussian edge detection (LoG, edgemode=1), valley detection (edgemode=2), none (edgemode=0), or both (edgemode=3). The parameters of the method are: edgeSigmaL and logthresh (for LoG), edgeSigmaV, valleythresh1 and valleythresh2 (for valley detection), crossthresh (for both).
• Segmentation testing tool. Click the Segmentation button on the 'Parameter test mode' panel to see segmented image for the current parameter set before cell detection. Try adjusting the parameters mentioned above and test the effect. See the Segmentation testing tool for more information.

4. Other shape-related parameters

• Area. The program rejects the cells larger and smaller than a certain thresholds, which are regulated by the areaMin and areaMax parameters and expressed in px². To adjust these parameters, zoom on a cell and estimate the area of a cell by counting the pixels along and perpendicularly to the cell. Typically set areaMax  somewhat larger than the largest cell and areaMin somewhat smaller than the smallest cell. If on other images some extreme cells get rejected, click on the largest/smallest detected cell to see its area and to estimate the area of these extreme cells.
• Smoothing cell. The program uses Fourier smoothing keeping a predefined number of descriptors, defined by fsmooth parameter. Typically for extremely small cells fsmooth should be ~ 10, for normal-size cells ~20, for spaghetti-like filamentous cells up to ~200. Values too small don't allow to fit a complex shape of a cell correctly. Values too large are more tolerable, but may result in a pixilated outline. Increasing this parameter is one of the ways (the only way for algorithm 1) to smooth the outline. To smooth the cell using algorithm 2 you can reduce the number of descriptors by setting Nkeep lower (from the default of 11 to 7-9), using algorithm 4 increase the outline rigidity (increase rigidityRange or rigidity parameters).
• scaleFactor. Use this parameter if you have adjusted well the parameter for a particular set (keeping scaleFactor=1), and then you only change the resolution slightly by using variable magnification units on the microscope. Increase or decrease this parameter proportionally to the resolution (measured in µm/px).
• Cell diameter (algorithm 4 only). Using algorithm 4 you have the direct control on two aspects of cell diameter: the absolute values and flexibility. Note, that you don't set the width exactly, it will still be adjusted to fit actual cells. The absolute value is regulated by cellwidth parameter (mesh generation is optionally regulated by an additional parameter meshWidth). Estimate this parameter by counting a typical number of pixels across a cell. The flexibility is regulated by wspringconst parameter. The default wspringconst=0.5 produces a very small error, which is tolerable for most purposes. However, if the exact value of the diameter is one of the properties you are trying to measure in your experiment, you should reduce this parameter to about 0.05 will be small enough for any purposes. If the image is noisy and the cell diameter variations are caused by image noise, consider increasing it up to about 2.
• Cell rigidity (algorithm 4 only, for filamentous cells). The rigidity of filamentous cells is regulated by rigidityB (elasticity between nodes, < 10, default: 4) and rigidityRangeB (number of affected nodes, < cell length, default: 7) parameters. Low values make the cell too 'flexible', producing kinks sometimes resulting in errors. High values smooth kinks too much, so that the program may lose the cell.

5. Aligning the shape

• Attraction/repulsion. Attraction is pulling the cell outline into the 'dark' (i.e. cell) areas to fill the cell completely. Repulsion is retracting the outline from 'light' (background) areas so that it does not extend from the cell. These effects help to fit better isolated cells, but may cause the shape to penetrate into the neighbor cell. The two effects are regulated by attrCoeff and repCoeff parameters. Typical values: from 0 to 1. Usually set them in the range 0-0.2 and adjust only if necessary. In most cases keep attrCoeff < repCoeff.

• Alignment testing tool. The 'alignment' button on the 'Parameter test mode' panel lets the user to see the process of alignment dynamically. After activating the mode, click any processing button (such as All frames, This frame, Range, buttons for manual operations) to see the how alignment happens. This regime when activated is equivalent to fitDisplay and fitDisplay1 parameters present and set to 1. Note, MicrobeTracker still saves the data in memory and to the disk after each frame (if selected), so be careful to not erase your data! See the Alignment testing tool for more information.

6. Splitting and joining cells

• Splitting cells is regulated by splitThreshold parameter (available in algorithms 2-4). This parameter defines the minimum septation depth in the profile of integrated phase contrast intensity (see the image to the right) which triggers cells splitting. If the condition is met, the program will try to fit new contours to each of the parts of the cells separated by the septum position. Note, if you split the cell manually, this condition does not have to be met. Typical values of the parameter ~ 0.3. The value 1 means that the cell will never be split. If the cells do not split when they should - reduce splitThreshold (to 0.12-0.25), if they split too early - increase it (to 0.4-0.5). 

• Joining cells. The program will attempt to join two cells if the distance between two poles is below joindist parameter and the angle between the axes of the cells at those poles is below joinangle parameter (in radians). If these conditions are met the program will try to fit a new shape to the two cells and check the splitting condition for the resulting cell. Only in this condition fails, the new cell outline will be kept. Note, if you join the cells manually no check for either of the conditions will be performed, however the program needs to be able to fit new shape to the both cells. Typical values are 5 pixels for the distance and ~ 1 radian for the angle, the values can be easily estimated by looking at a highly zoomed image.