The minimum distance between local maxima of the smoothed image that will be used as fit start locations. If a weak maximum occurs within this distance of a strong maximum inside the smoothed image, the weak maximum is ignored and no fitting is attempted at the weak maximum.
High values will ensure a higher noise tolerance at the cost of some missed localizations.
If this option is set, all planes are fit independently of each other. Spot finding and spot fitting will happen in each plane, and the results will be put into one large localization vector. The fluorophore field in the output indicates in which plane a localization was found.
Set the number of fluorophores to 1 if you do separate fitting.
The fit judging method controls the decision whether a set of fitted PSF parameters is a localization or just background noise.
See Also Fixed global threshold.
The spot fitting method is the method for converting suspected fluorophore positions (spots) into localizations. There is currently only one useful choice, Levenberg-Marquardt fitter.
Select a smoothing method to be employed before selecting local maximums as spot candidates. The standard method here is smoothing with an average mask (Spalttiefpass), which gives good performance for most images. Median smoothing provides slower, but sometimes more accurate and less blurring smoothing. Erosion (also known as local minimum filter) is faster than the median filter and gives similar results for small (standard deviation close to 1) spots, while the fillhole transformation followed by erosion is better for large spots. For a complete discussion and quantitative comparison, see [WolterDiplomarbeit] and [Wolter2010].
The maximum distance between the start X/Y position and the final X/Y position for the fit over the entire fit process. If the positions differ more than this amount, the localization is discarded.
The rapidSTORM engine uses dynamical thresholding, i.e. fits the spots with at the most intense positions in the smoothed image first and continues in order of decreasing intensity. Fitting is aborted when a number of spots equal to the motivation is rejected by the Fit judging method. This parameter controls the motivation.
Higher values in this field will cause more localizations to be found, albeit at the cost of more false positives.
Treat the PSF width as variables in the fit process rather than as constants. The estimated or fixed standard deviation parameters act as initial values for the estimation when the free covariancce matrix is selected.
This checkbox will drastically reduce the localization precision and increase noise localizations, but is useful when the PSF width is variable between spots (e.g. in 3D estimation). If it is merely unknown, you should prefer using the Estimate PSF form output.
See also: PSF model
When performing two-kernel analysis (see the section called “Two-kernel analysis”), any double-kernel fit with the two kernels further apart than this number is immediately discarded, resulting in a two-kernel improvement of 0.
This parameter ensures that large fitting windows and two-kernel analysis can cooperate.
See Also Compute two kernel improvement.
The nonlinear fit process for a localization attempt is continued while the lateral mean position (x,y) changes absolutely by more than this parameter.
See Also Fit iteration limit, Relative epsilon.
The nonlinear fit process for a localization attempt is continued while any parameter (except the lateral means x and y) changes relatively by more than this parameter.
See Also Fit iteration limit, Lateral epsilon.
The usual lambda factor of Levenberg-Marquardt fitting controls the size of the trust region for Gauss-Newton steps. Refer to a good textbook for its meaning, e.g. [Recipes].
The nonlinear fit process for a localization attempt is stopped after this number of iterations.
See Also Relative epsilon, Lateral epsilon.
Set the psffwhmx and psffwhmy fields of localizations to the widths used in computation. If this field is checked, the localization output files will contain PSF width information, and all outputs working with localization widths depend on this checkbox.
Fit the lateral emitter position (x,y) in each plane independently. This option can mitigate small errors in alignment at the cost of reduced precision.
Fit the emission intensity in each plane independently. This option can be useful if the number and nature of fluorophore populations in the sample is unknown. However, it will break multi-colour inference, and all Transmission of fluorophore N fields should be set to 1.
Perform the the section called “Two-kernel analysis” computation, which sets the two kernel improvement field.
After successfully fitting a spot with a least squares error model, improve the fitted position using a maximum likelihood error model. This improves precision, especially for low photon counts, in exchange for a considerable increase in computation time. The Camera response to photon and Dark intensity fields must be set if this option is used.
This fit judging method judges parameter sets by their intensity. If the intensity surpasses the threshold, the parameter set is counted as a localization, and discarded otherwise.
See Also Intensity threshold.
Minimum fitted emission intensity necessary for a spot to be considered a localization. If the fitted position has an intensity lower than this value, it is discarded as an artifact.
See Also Fixed global threshold.
This fit judging method judges parameter sets by their intensity and the local background. Both values are the estimations from fitting the PSF model to the data. If the ratio of intensity to square root of local background surpasses a threshold, the parameter set is counted as a localization, and discarded otherwise. The square root of the background is used because it estimates the standard deviation of a Poisson-distributed background. The Dark intensity and the Camera response to photon should be set to use this option.
See Also Signal-to-noise ratio.
Minimum ratio of emission intensity to square root of background signal intensity necessary for a spot to be considered a localization.
See Also Local relative threshold.
All pixels within this radius of a spot are used for fitting. The selected pixels form the data points for the nonlinear fitting routine, and the PSF is fitted to their intensities.
A larger value here allows more precise fitting at the cost of slower computation.
Employ the computational optimization of separating the X and Y dimensions of the Gaussian for computing the function's derivatives. This optimization is only performed if the alignment is set to No alignment, but can drastically improve computation time for large fit windows.
Compute values and derivatives of the PSF with 64 bit wide floating point numbers instead of 32 bit. Ensures higher reliability and precision, but with a small speed cost.
The full width of a square structuring element used for Smooth by median and Smooth by average smoothing.
The standard deviation (σ) of a Gaussian smoothing kernel.
The full width of a square erosion mask.
The full width of a square structuring element for the background averaging.
The full width of a square structuring element for the foreground averaging.
Currently, Levenberg-Marquardt fitting is the only implementation of a spot fitter, i.e. a routine that localizes a fluorescence emission to subpixel precision. The LM fitter works by building a PSF model (in most cases a Gaussian function), estimating crude initial guesses for a the parameter of this model, and then optimizing the distance between the data in the immediate surroundings of the spot and the theoretical model. The parameters of the model then give the location of the emitter and its intensity.
Smooth the input images with a Gaussian kernel of the specified width. This kernel can be set to the PSF size or specified independently. Gaussian smoothing is often suboptimal, see [WolterDiplomarbeit] for details.
Smooth the input by performing a morphological fillhole transformation (using reconstruction by dilation) followed by a rectangular erosion.
Smooth by applying a square median filter of the specified width.
Smooth by applying a morphological erosion with a square structuring element of the specified size.
Smooth by applying a square moving window average filter. Then substract the result of a wider square moving window average filter, which estimates the local background and can thereby deal with uneven backgrounds.
Smooth by applying a square moving window average filter.