Java

This plugin permit to measure the signal spread of a molecule with respect to the cell area.

An ImageJ plugin for manually tracking objects by mouse clicking. 

This plugin is bundled with Fiji. 

This plugins allows debleaching of time sequences of fluorescence images.

Bundled with Fiji. "Do all" is the great feature...

This plugin shows the color distribution within a 3D-color-space. Extensive documentation is available atwww.f4.fhtw-berlin.de/~barthel/ImageJ/ColorInspector/help.htm.

Color Inspector 3D is also available as a stand-alone program that uses ImageJ as a library. To run it, download ColorInspector3D.jar and double click on it. On Windows, Java 5.0 or later must be installed

Choose the best auto thresholding technique for your data. 

View a single channel of a 2D image as a 3D elevation map (X,Y,Intensity).

Fill holes in a binary image.

Edge detection by Deriche's method.

Shows how to listen active sequence / viewer events.

PSF Generator is a software package that allows one to generate and visualize various 3D models of a microscope PSF. The current version has more than fifteen different models.

3D diffractive models: scalar-based diffraction model Born & Wolf, scalar-based diffraction model with 3 layers Gibson & Lanni, and vectorial-based model Richards & Wolf, and Variable Refractive Index Gibson & Lanni model.

Defocussing a 2D lateral function with 1D axial function: the available lateral functions are: "Gaussian", "Lorentz", "Cardinale-Sine", "Cosine", "Circular-Pupil", "Astigmatism", "Oriented-Gaussian", "Double-Helix".

Optical Transfer Function generated in the Fourier domain: Koehler simulation, defocus simulation.

This tutorial explain how to use a deviation renderer JFreeChart chart in Icy.

Quote from the ImageJ wiki:

The Stitching Plugin (2d-5d) is able to reconstruct big images/stacks from an arbitrary number of tiled input images/stacks, making use of the Fourier Shift Theorem that computes all possible translations (x, y[, z]) between two 2d/3d images at once, yielding the best overlap in terms of the cross correlation measure. If more than two input images/stacks are used the correct placement of all tiles is determined using a global optimization. The stitching is able to align an arbitrary amount of channels and supports timelapse registration. To remove brightness differences at the tile borders, non-linear intensity blending can be applied.

The Image Stitching package comes with 2 different plugins:

• Pairwise Stitching: Stitch two 2d-5d images, rectangular ROIs can be used to limit the area to search in.
• Grid/Collection Stitching: Stitch an arbitrary amount of 2d-5d input images. It supports cases where the approximate alignment is known (grid, stored in file, metadata) as well as completely unguided alignment.

Simple chronometer for Icy.

This plugin is bundled with Fiji. For installation in ImageJ1, download from the link below and manually install the class file. 

Quote:

The colour deconvolution plugin (java and class files) for ImageJ and Fiji implements stain separation using Ruifrok and Johnston's method described in [1]. The code is based on a NIH Image macro kindly provided by A.C. Ruifrok.
The plugin assumes images generated by colour subtraction (i.e. light-absorbing dyes such as those used in bright field histology or ink on printed paper). However, the dyes should not be neutral grey (most histological stains are not so).
If you intend to work with this plugin, it is important to read the original paper to understand how new vectors are determined and how the procedure works.
The plugin works correctly when the background is neutral (white to grey), so background subtraction with colour correction must be applied to the images before processing.
The plugin provides a number of "built in" stain vectors some of which were determined experimentally in our lab (marked in the source with GL), but you should determine your own vectors to achieve an accurate stain separation, depending on the stains and methods you use. See the note below.
The built-in vectors are :

• Haematoxylin and Eosin (H&E) x2
• Haematoxylin and DAB (H DAB)
• Feulgen Light Green
• Giemsa
• Fast Red, Fast Blue and DAB
• Methyl green and DAB
• Haematoxylin, Eosin and DAB (H&E DAB)
• Haematoxylin and AEC (H AEC)
• Azan-Mallory
• Masson Trichrome
• Alcian blue & Haematoxylin
• Haematoxylin and Periodic Acid - Schiff (PAS)
• RGB subtractive
• CMY subtractive
• User values entered by hand
• Values interactively determined from rectangular ROIs

Draws a contour plot on top of a sequence.

The Trainable Weka Segmentation is a Fiji plugin that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Weka (Waikato Environment for Knowledge Analysis) can itself be called from the plugin. It contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. As described on their wikipedia site, the advantages of Weka include: - freely availability under the GNU General Public License - portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform - a comprehensive collection of data preprocessing and modeling techniques - ease of use due to its graphical user interfaces - Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.

The main goal of this plugin is to work as a bridge between the Machine Learning and the Image Processing fields. It provides the framework to use and, more important, compare any available classifier to perform image segmentation based on pixel classification.

The plugin performs stitching of images of a tiled scan to reconstruct the image of the whole sample.

KNIME is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting. Its an open integration platform and provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network. One of these extensions adds the ability for image analysis allowing to process, segment and further analyze images which can easily be used in combination with the other extensions, potentially from other fields.