Linux

Microscopy Image Browser (MIB) is a high-performance Matlab-based software package for advanced image processing, segmentation and visualization of multi-dimensional (2D-4D) light and electron microscopy datasets.

MIB is a freely available, user-friendly software for effective image processing of multidimensional datasets that improves and facilitates the full utilization of acquired data and enables quantitative analysis of morphological features. Its open-source environment enables fine tuning and possibility of adding new plug-ins to customize the program for specific needs of any research project.

AnaMorf is a plug-in developed for the ImageJ platform (rsb.info.nih.gov/ij) to analyse the microscopic morphology of filamentous microbes. The program returns average data on a population of mycelial elements, using the descriptors projected area, circularity, total hyphal length, number of hyphal tips, hyphal growth unit, lacunarity and fractal dimension. The plug-in accepts as input a user-specified directory of images, analysing each and outputing tabulated results.

Kappa is a Fiji plugin for Curvature Analysis.

It allows a user to measure curvature in images in a convenient way. You can trace an initial shape with a B-Spline curve in just a few clicks and then fit that curve to image data with a minimization algorithm. It’s fast and robust.

iTrack4U is a Java-based software using ImageJ and jMathPlot libraries, which aims at automatically tracking cells recorded in phase-contrast microscopy. It includes all tools from image files preprocessing, tracking to data extraction and visualization. 

Please cite Cordeliéres et. al. (2013) when using this software package!

This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length.

Tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See Skeletonize3D for an example of how to produce skeleton images.

The voxels are classified into three different categories depending on their 26 neighbors: - End-point voxels: if they have less than 2 neighbors. - Junction voxels: if they have more than 2 neighbors. - Slab voxels: if they have exactly 2 neighbors.

End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.

Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other.

Output data type: table result, image of the skeleton