Automated

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This is a simple example of a DNA damage assay using single cell gel electrophoresis. Here, the measurement of interest is the length and intensity of the comet tail. Also, illumination correction is used to reduce background fluorescence prior to measurement. Also shown is a silver-stained comet example in which the percentage of DNA contained in the tail is calculated.

Example Images: Packaged together with the cellprofiler pipeline file. 

Oufti (previously named MicrobeTracker) is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. Lesterlin et al. (2014).

The Outfi Forum is quite active.

This workflow classifies objects based on object-level features (e.g. intensity based, morphology based, etc) and user annotations. It needs segmentation images besides the raw image data. Segmentation images can be obtained from ilastik pixel classification, or binary segmentation images from other tools. Within the object classification, one can prefilter objects through thresholds (on pixel probability image) or object sizes (on segmentation image). Outputs are predicted classification label images. Selected features can also be exported. Advanced users also have possibilities to add customized (object) features for classification in a simple plugin fashion through python scripts.

Simple spatial filters can be used to suppress noise in raw image data (i.e. by averaging intensities). The best choice of filter depends on the nature of the noise, but Gaussian filtering works well for Poisson noise (i.e. commonly observed photon-counting shot noise); whereas a median filter is ideal for salt-and-pepper noise. A larger filter radius leads to stronger noise suppression but more blurring. The URL above describes the simple 2D spatial filters available in ImageJ, but similar filters are available in most software. For 3D data, 3D versions of these filters work best (since there are more pixels to average within the same radius).