Background estimation for deconvolution

In the deconvolution wizard in Huygens Essential and Huygens Professional, estimating the BackGround is part of the deconvolution procedure. After the estimation you can indicate a percentage of correction to be applied to the automatic value. For example, set this to zero to accept the estimated value as is, or set it to -10 to reduce the estimated value in 10%. If you manually modify the background absolute value, the change will be also reflected in the percentage correction. This relative background value is especially useful for timeseries, which intensity could decay due bleaching.

In Huygens Professional, there are multiple ways you can estimate the BackGround:

Automatic estimation

The mean background can be estimated automatically in Huygens Professional with the Estimate background tool in the Analysis menu of the Operations window. Use a search area of 0.7 micron radius. If your image does not contain large open areas, decrease the radius.

This tool reports the found value to the Task report window along with the coordinates of the area in which the value was found, so you that can confirm this value visually in e.g. the Twin Slicer.

The reported value is a conservative estimate of the average background. For instance, when there is appreciable bleaching there is high probability that the tool will find the last recorded, darkest layer. You can exclude such layers from the search by cropping the image.

The Estimate background tool tries to evade off focus light of the object in conventional images. Still, it is a good idea to inspect the location were the tool reported the lowest average value.

This automatic estimation will be executed also if you run a deconvolution command like cmle or qmle with any Background Mode other than manual, but you can correct the estimated values by a percentage as defined by the Background Per Channel parameters.

Selecting a region of interest

You can have more control if you extract the interesting portion of the image to a new image by using the Intelligent Cropper, and the ask for the statistics of that independent chunk (Edit > Statistics): you will obtain among other things the average value. Still, the cropper only allows you to crop or extract regions of cuboid shape.

But the ROI selection in the Object Analyzer can be used for more complex shapes. When you have a ROI defined anywhere in the image and click the "Analyze all" button, also information about the ROI is printed on the table, among others (by default, but you can change this) the sum intensity and the number of voxels. By dividing these two figures you can easily get an average value for the intensity inside the ROI.

Because defining a ROI in the background where not objects are normally found may by tricky, you may want to activate in the MIP pipe the same channel you are analyzing, so that you have a visual reference on where to define the ROI.

See Object Analyzer ROI.

Background estimation within the Colocalization Analyzer

For the colocalization analyzer there are three methods to estimate the background:
  • Gaussian minimum (which is equal to the method explained above)
  • Costes method
  • Optimized method

The big difference between the gaussian minimum and the costes/optimised methods is that the gaussian minimum only looks at the information of a single channel. Costes and optimised method use the information of two channels to find the optimal background setting.

Costes method

This method is based on the estimation method explained in article Automatic and Quantitative Measurement of Protein-Protein Colocalization in Live Cells by Sylvain V. Costes, Dirk Daelemans, Edward H. Cho, Zachary Dobbin, George Pavlakis and Stephan Lockett, Biophysical Journal, volume 86, June 2004 (3993-4003).

This method calculates the regression line of the 2D-histogram or 2D scatterplot and each point on this line is a combination of backgrounds for the red and green channel. Starting with the highest point on the regression line, the position is decreased over the regression line, until the Pearson coefficient of the background is zero. In other words, until the Pearson coefficient of the voxels that are below the background threshold is zero.

Optimized method

This method continues with the Costes method without the assumption that the ideal background threshold combination is on the regression line. By using an iterative process, the entire histogram is searched to find the point such that the Pearson of the background is zero (or closest to).

The assumption that is made for both the Costes as the Optimised method is that the noise in a constant background is Poission distributed. The Pearson coefficient of two channels with Poission noise is zero.

To see how the values of these methods are used when setting a threshold in the Colocalization Analyzer, please visit the section on Threshold handling on our Colocalization Theory page.