Automated

Process an image using Perreault’s modern constant-time median filtering algorithm.

This tutorial explain how to create an intensity profile over an ROI.

Enables Python scripts that are run inside Icy to communicate with other Python instances outside Icy. This allows the access to CPython-only libraries such as Numpy.

Resources requiring this include: CalloseCounter, BioFlow, EvaFE In Python

A canvas plugin using JFreeChart to show intensity profile of a specific row or column. Initially used in a 1-row image of 1D signal representation. Can also used with Micro-Manager for Icy Plugin to show 1D signal from hardware like data acquisition device as waveform. Common 2D+ sequence is also supported. Using it by select the plugin icon in the canvas type combobox of the sequence window, a JFreeChart line chart will show up. As an alternative plugin to Intensity Profile, this plugin is modified from "Intensity Profile" by fab, special thanks to him for giving me the idea to make such a plugin.

MonogenicJ performs multiresolution monogenic analyses of 2D images. It extracts wavelet-domain features that characterize the local orientation, the phase and the dominant frequency of an image patch at various levels of resolution.

Wavelet-based method to merge a stack of micrographs taken at different focal positions (aligned along the optical axis) into a single, entirely focused composite image.

This plugin allows the creation of custom animations for 3D viewing. It will generate a new sequence that can be edited in Icy, and saved.

The animation is based on key framing, as in most of 3D rendering software projects.

Layer for the 3D Viewer, allowing the user to move the camera in a more intuitive way.

Manual angle measurements. 

No javadoc accessible, but can be downloaded from the webpage. 

Mice Profiler tracks multiple mice from a top view video.

Mice Profiler uses geometrical primitives to model and track two mice without requiring any specific tagging. The program monitors a comprehensive repertoire of behavioral states and their temporal evolution, allowing the identification of key elements that trigger social contact.

Mice Profiler Label Analyser performs temporal analysis of data from Mice Profiler.

This C routine is based on the following two papers:

• M. Unser, A. Aldroubi and M. Eden, "B-Spline Signal Processing: Part I--Theory," IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 821-832, February 1993.
• M. Unser, A. Aldroubi and M. Eden, "B-Spline Signal Processing: Part II--Efficient Design and Applications," IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 834-848, February 1993.

It implements the resampling of an image/volume under an affine transformation. The continuous model is based on splines of degree 0 (nearest neighbours), degree 1 (linear interpolation), degree 2 (quadratic), 3 (cubic), 4 (quartic), 5 (quintic), 6 and 7. By convention, the affine transformation is given by an homogenous matrix; the operation performed is output(A x) = input(x). In other words, a matrix given by

A = { {2,0,0,0}, {0,2,0,0}, {0,0,2,0}, {0,0,0,1} }

will result in a magnification by a factor 2 in linear dimensions. In case the desired operation would be output(x) = input(A x), it should be easy to modify the code (mainly: remove the call to invertTrsf() and assign invTRsf = trsf). The origin relative to which the transformation is performed is given with respect to the center of the top-upper-left voxel; the coordinate system is right-handed. Output values in need of extrapolation are set to the value background.

The input volume (the volume to transform) is given by inPtr, a pointer to an array of float values in raster order. More precisely, the values are ordered such that the x values are incremented first, then the y values, finally the z values. The size of the volume is given by nx, ny and nz, respectively. The output volume has necessarily the same size and follows the same organization. Its memory space cannot be shared with the input, and is supposed to be already allocated when the affineTransform() routine is called.

All routines are local, with the exception of the routine to call, named affineTransform(), and the routine errorReport(). The latter is not included in this distribution; its purpose is to display an error message given by a C-string. Else, the code is self-contained (provided a standard ANSI-C environment is available). It consists of only two files: affine.h and affine.c.

Outline The incessant development of improved microscopy imaging techniques, as well as the advent of highly selective fluorescent dyes has made possible the precise identification of tagged molecules in almost any biological specimen. Of particular interest are the visualization and the study of living cells, which induce tight constraints on the imaging process. To avoid the alteration of the sample and to achieve a high temporal resolution, low fluorophore concentrations, low-power illumination and short exposure time need to be used in practice. Such restrictions have a tremendous impact on the image quality. This is why we have recently introduced a new method, coined PURE-LET [1,2,3], for efficient, fast, and automatic denoising of multidimensional fluorescence microscopy images.

This plugin calculates the optic flow for each pair of images made with the given stack.

Automatically segment the boundary of a nucleus or cell starting from an approximate ROI. Supports 2D and 3D images and tracking of slowly moving cells. Ideal to study cell morphodynamics.

Six 2D differential operations are implemented in this ImageJ plugin. - Gradient Magnitude - Gradient Direction - Laplacian - Largest Hessian - Smallest Hessian - Hessian Orientation