2D

A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin **Squassh** is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

The linked webpage presents a collection of ImageJ macros for Intelligent Imaging (Feedback to microscope system for the secondary scan). 

An ImageJ macro able to control some microscopes (Micro-manager or Leica CAM controlled) to acquire high resolution images of only some structures (e.g. isolated cells) or events (e.g. mitosis) within a sample. The scan is sequenced as a primary (low resolution monitoring) scan and a secondary (high resolution, multi-dimensional) scan.

A Jython script using the plugin : Register Virtual Stack Slices It takes a sequence of image slices stored in a folder, and delivers a list of registered image slices (with enlarged canvas). One of the images in the sequence can be selected by the user as reference and it will remain intact. The plugin can perform 6 types of image registration techniques: - Translation - Rigid (translation + rotation) - Similarity (translation + rotation + isotropic scaling) - Affine - Elastic (via bUnwarpJ with cubic B-splines) - Moving least squares All models are aided by automatically extracted SIFT features.

The rapidSTORM project is an open source evaluation tool that provides fast and highly configurable data processing for single-molecule localization microscopy such as dSTORM. It provides both two-dimensional and three-dimensional, multi-color data analysis as well as a wide range of filtering and image generation capabilities. The general operation of rapidSTORM is described in Wolter et al (2012).

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