Component

The ImageJ pligin, called PixFRET, allows a simple and rapid determination of channel bleed-through parameters and the display of normalized FRET images. see 2521

Input data type: 

Stacks with 2 channels (controls for bleed-through calculation) or 3 channels (FRET calculation)

Output data type: 

One Image with FRET values and One image with normalized FRET values

as of 20180529, links are not working due to web defacement.

An example macro introduced in the documentation page of the ImageJ plugin Trainable Weka Segmentation (in Fiji, it's bundled). A segmentation protocol based on machine learning. Full macro is available in the "Download" Link. 

This plugin can be trained to learn from the user input and perform later the same task in unknown (test) data. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka. Segmentation: it provides a labeled result based on the training of a chosen classifier. Trainable Weka Segmentation Complete macro example is at the end of the page.

A clear tutorial for splitting connected particles (cells) in a binary mask.

A deconvolution component applicable to confocal and STED microscopy. The MATLAB function fo this package implements the SGP method for n-dimensional object deblurring with the option of boundary effects removal. Although this is a preliminary version, results seem to be good from their paper (Zanella et al 2013).