Java

Manual Tracking GUI. Many shortcut keys, and after being experienced, manual tracking can efficiently done. Post-editing capability to delete segments, merge and splitting tracks is quite useful.

Fluorescence spectroscopy by image correlation is a technique that allows analysing and characterizing the different molecular dynamics from a sequence of fluorescence images. Many image correlation techniques have been developed for different applications but in particular to study the mechanisms of cell adhesion during migration. These techniques can be used with most imaging modalities: e.g. fluorescence widefield, confocal microscopy, TIRFM. They allow to obtain information such as the density in molecules, diffusion coefficients, the presence of several populations, or the direction and speed of a movement corresponding to active transport when spatial and temporal correlations are taken into account (STICS: Spatio-Temporal Image Correlation Spectroscopy).

This plugin is based on ICS_tools plugin by Fitz Elliott, available here.

Some bugs have been removed, ROI does not need to be squared, fitting is weighted in order to give more weight to the smaller lags (temporal or spatial)

Exemple of use on sample data [fluorescent beads](http://biii.info/node/2577 "Beads") - Select an ROI, start by ICS to get the right PSF size - Then run TICS and select diffusion, or diffusion plus flow model. Remove the first line (autocorrelation) which corresponds to the noise autocorrelation before fitting.

HDF5 is a data format for storing extremely large and complex data collections. This Fiji/ImageJ HDF5 plugin saves and loads 2D - 5D datasets with flexible options.

In Fiji, the plugin is downloadable via update site "HDF5".

ImageJ plugin to analyze changes in vessel diameters, described in Fernández er al (2014). More specifically the paper describes the measurement of isolated retinal arterioles (ca 50 micrometer diameter) but can be used for diameter measurements of similar vessel structures.

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.