plugin

A Java Package for Geometrical Image Transformation, works up to 5D.

• Affine
• Crop
• Embed
• Matrix
• Mirror
• Rotate
• Scale
• Translate
• Turn

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.

An often used Laplacian filter for enhancing signals at object boundaries and dots. It works with XY, XYZ, XYZ-T, XYZ-T-Ch1, XYZT-C1-C2 images. Distributed as a part of ImageJ plugin FeatureJ, and included in Fiji. The second URL above is the link to its Javadoc. (imagescience.feature.Laplacian). A primer for using this class in Jython script is in CMCI Jython/Fiji cookbook: FeatureJ.

The FindFoci plugins allow the identification of peak intensity regions within 2D and 3D images. The algorithm is highly configurable and parameters can be optimised using reference images and then applied to multiple images using the batch mode. Details of the benefits of training an algorithm on multiple images can be found in the FindFoci paper: 2591

The Fourier transform of an image produces a representation in frequency space: i.e. separated according to spatial frequency (effectively scale). The 2D amplitude map of the different spatial frequencies is symmetrical, and is commonly displayed with low spatial frequencies (large features) in the centre, highest spatial frequencies (small features) at the edges. Fourier filtering involves suppressing or enhancing features in the Fourier domain before carrying out an inverse Fourier transform to obtain a filtered real-space image. ImageJ's _Process > FFT > Bandpass Filter_ implements two common Fourier-filtering functions: 1. filtering for specific sizes of feature in an image by selecting minimum and maximum feature sizes (selecting a radial band of frequencies in Fourier space); 2. filtering out repetitive horizontal or vertical stripes by cutting out a zero-frequency stripe in the orthogonal direction in frequency space. The example image above shows the effect of filtering for 2 feature size ranges: 0-8 pixels, and 8-256 pixels; where the former appears "flattened" or washed-out, and the latter very blurred. The small images displayed to the lower-right of each filtered image correspond to the mask applied to the Fourier transform. Such filtering can be useful prior to global thresholding, for noise suppression, etc.

Implementation of some image correlation spectroscopy tools

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.

The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in Chanouard et al (2014) as "method 9" and is described in detail in its supplementary PDF (page 65).

Dependencies

Following plugins are required.

1. JAR to be placed under IJ plugin directory
2. A pdf file with instructions and output description is also available in the zip .
3. MTrackJ : Used for visualization of tracks. Preinstalled in Fiji.
4. Imagescience.jar: This library is used by MTrackJ. Use update site to install this plugin.
5. jama.jar. Preinstalled in Fiji.

##Advantages:

• support blinking (with a parameters allowing it or not)
• fast,
• can be used in batch, some analysis results provided.
• No dynamic model.
• The tracking part is not dependent of ImageJ.

Pitfalls:

• does not support division
• the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs.
• No Dynamic model (so less good results but can be used for a first study of the kind of movements)

##The sample data

The parameters used for this example data Beads, were

1. detection: 150
2. the max distance in pixels: 20
3. max allowed disappearance in frame: 1

The root tools help to efficiently measure the following characteristics of plant roots: the angle of the opening of the whole root the depth to which it goes down the number of roots at multiple depths (for example 30cm, 35cm, ...) the diameters of the roots at multiple depths (for example 30cm, 35cm, ...)

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".