plugin

The aim of this plugin is to characterise the orientation and isotropy properties of a region of interest (ROI) in an image, based on the evaluation of the gradient structure tensor in a local neighborhood. 

Compute and display the histogram of a sequence, with a more accurate control on the histogram parameters (such as the number of bins) than the built-in Icy widget. In particular, the histogram can be computed either over a whole sequence or over a sub-region defined by a ROI.

For post-processing analysis, use Analyze Skeleton plugin written by the same author.

Grab random images from Flickr :

• in recent uploads
• in interestingness stream
• from tags search

FlowJ is a collection of popular 2D optical flow algorithms, Lucas and Kanade, Uras, Fleet and Jepson, and Singh, in Java.

Bundled with Fiji, but can also be simply installed to ImageJ as well.

The plugin Landmark Correspondences calculates a transformation between two corresponding landmark clouds and renders a transformed image. The landmarks are read from point selections over two images. The transformation is estimated by a least squares or Moving Least Squares fit for the available models.

TrakEM2 is an ImageJ plugin for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation (Fiji comes with TrakEM2). It supports arbitrary-sized datasets. 

This plugin simulates color blindness. 
It is based on http://quarkphysics.ca/phys1/light/u-light.htm

This ImageJ plugin segments the image in classes by thresholding. It uses the same algorithm found in Otsu Thresholding, but was adapted to output more than 2 classes out of the process.

An ImageJ plugin (author: Kurt De Vos) for counting multiple cell classes manually, which overlays already-counted cells on the image. It is controlled via its own graphical user interface, and can export and load results.

Identify objects (as nuclei) within an image without needing the assistance of another cellular feature (as cell). 

Use Icy as an image viewer from Matlab.

Layer for 3D View that displays a Bounding Box around the object.

This is a set of Matlab routines for computing generalized Daubechies wavelet filters.

ImageMath performs simple mathematical operations on image intensities, like addition, subtraction, multiplication, division...

Label images according to their position in plates. 

The plugin analyses a point pattern (positions of objects of interest) distributed within a reference structure.

This analysis allows in particular to assess deviation from spatial randomness, and to reveal trends for clustering (attraction) or regularity (repulsion). No edge correction is performed, as it is assumed that no point is expected outside the reference structure.

This plugin comes together with 3D ImageJ suites plugin.

NuclearQuant is a QuantCenter module. It is designed for cell nuclei detection and quantification of IHC stained samples. NuclearQuant measures several morphological features besides stain intensity. The cell nuclei classification and the final score are calculated by the intensity score and the proportion score.

This plugin calculates the Nyquist sampling, in the radial and axial direction for your Microscope.

The fractional splines are an extension of the polynomial splines for all fractional degrees α > -1. Their basic constituents are piecewise power functions of degree α. One constructs the corresponding B-splines through a localization process similar to the classical one, replacing finite differences by fractional differences. The fractional B-splines share virtually all the properties of the classical B-splines, including the two-scale relation, and can therefore be used to define new wavelet bases with a continuously-varying order parameter

A MATLAB package is made available for computing the fractional smoothing spline estimator of a 1D signal, and for generating fBms (fractional Brownian motion).

The smoothing is applied by the way of a bi-exponential filter, itself realized by a pair of one-tap recursions. It is therefore very fast; moreover, its computational cost is truly independent of the amount of smoothing. Meanwhile, the preservation of edges is obtained by a range filter akin to the range filter found in a bilateral filter. More technical details are available here.

The plugin allows one to control the amount of smoothing, the type of range filter, its broadness, and to iterate the filter several times if desired. We illustrate in Figure 2 a possible outcome of this filter. Here, we iterated the BEEPS 10 times with a Gaussian range filter, σ = 10, and the spatial decay λ = 0.1.

JRuby script that will take an image stack and generate from it an image that should appear in 3D when viewed through red and cyan glasses. All that this does is to do two maximum intensity projections from two slightly different angles and merges them together.

The purpose of this plugin is to create a text file with a list of files from Gatan’s 3View montage image stacks. This text file can then be used to automatically import all the images into TrakEM2, as they are, stored in the original directories.

This plugin perform various 3D filters on 8-bits or 16-bits gray-levels stacks :

• 3D median

• 3D mean

• 3D minimum

• 3D maximum

• 3D maximum local

• 3D tophat (detect bright spots, TH=I-max(min(I)) )

3D viewer provides hardware-accelerated 3D visualization of image stacks as volumes, surfaces and orthoslices.

This plugin registers (= aligns, matches) a stack of image slices.

Fast Fourier Transform (FFT) for 2D/3D images.

Calibrator for Pixel Size in Calibrator manager.

Short Description

Execute some simple math operations on sequences, such as addition, product, absolute value extraction, rounding to the closest integer, etc. All the operations are executed pointwise.

Produces files that allow individual batches of images to be processed separately on a cluster of computers.

FlipAndRotate flips (mirror image) and/or rotates an image

This module identifies objects that are contained within in a grid pattern, allowing you to measure the objects using Measure modules. It requires you to have defined a grid earlier in the pipeline, using the DefineGrid module. For several of the automatic options, you will need to enter the names of previously identified objects. Typically, this module is used to refine locations and/or shapes of objects of interest that you roughly identified in a previous Identify module. Within this module, objects are re-numbered according to the grid definitions rather than their original numbering from the earlier Identify module. If placing the objects within the grid is impossible for some reason (the grid compartments are too close together to fit the proper sized circles, for example) the grid will fail and processing will be canceled unless you choose to re-use a grid from a previous successful image cycle.