plugin

The original paper describes a method to analyze mitochondrial morphology in 2D and 3D.

Marker-controlled Watershed is an ImageJ/Fiji plugin to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D based on the marker-controlled watershed algorithm (Meyer and Beucher, 1990). This algorithm considers the input image as a topographic surface (where higher pixel values mean higher altitude) and simulates its flooding from specific seed points or markers. A common choice for the markers are the local minima of the gradient of the image, but the method works on any specific marker, either selected manually by the user or determined automatically by another algorithm. Marker-controlled Watershed needs at least two images to run: The Input image: a 2D or 3D grayscale image to flood, usually the gradient of an image. The Marker image: an image of the same dimensions as the input containing the seed points or markers as connected regions of voxels, each of them with a different label. They correspond usually to the local minima of the input image, but they can be set arbitrarily. And it can optionally admit a third image: The Mask image: a binary image of the same dimensions as input and marker which can be used to restrict the areas of application of the algorithm. Set to "None" to run the method on the whole input image. Rest of parameters: Calculate dams: select to enable the calculation of watershed lines. Use diagonal connectivity: select to allow the flooding in diagonal directions.

Morphological Segmentation is an ImageJ/Fiji plugin that combines morphological operations, such as extended minima and morphological gradient, with watershed flooding algorithms to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D. Morphological Segmentation runs on any open grayscale image, single 2D image or (3D) stack. If no image is open when calling the plugin, an Open dialog will pop up. The user can pan, zoom in and out, or scroll between slices (if the input image is a stack) in the main canvas as if it were any other ImageJ window. On the left side of the canvas there are three panels of parameters, one for the input image, one with the watershed parameters and one for the output options. All buttons, checkboxes and input panels contain a short explanation of their functionality that is displayed when the cursor lingers over them. Image pre-processing: some pre-processing is included in the plugin to facilitate the segmentation task. However, other pre-preprocessing may be required depending on the input image. It is up to the user to decide what filtering may be most appropriate upstream.

This macro and plugins suite for ImageJ (and Fiji) serves to measure the velocity of moving structures and visualize them, from image time series (2D over time).

The module can be installed in ImageJ as a Macro Menu and each function/component can be called separately. The full workflow consists in calling some, or all, the functions sequentially in order to get from the image preparation (e.g. filtering and visualization of tracks) to the production of the kymographs (time vs. distance plot) and their analysis (retrieving the velocities).

Here is the full workflow sequence:

• Load image sequence
• Crop and time-filter the image sequence ("Walking average" plugin)
• Generate tracks by z-projection ("Stack difference" plugin)
• Select tracks and restore them in the original stack.
• execute plugin "multiple kymograph"
• Analyse: select edges of moving tracks graphically and quantify movement in a table.

input: 8-bit, 16-bit stacks, 2D in time. Calibrated is better for meaningful velocity measurements.

ouput: the kymograph image, the velocity measurements tables.

Requires ImageJ version: 1.33.n minimum.

Example of applications:

• velocity of moving objects/ structures with sharp edges, incl. the velocity of microtubules (and their plus ends),
• the velocity of vesicles or particles along a 2D path
• the velocity of migration of the edge of a cell or a multicellular group
• retraction velocity of contractile bundles (e.g. actin fibers) or multicellular tissues after mechanical disruption (e.g. laser surgery)

An ActionBar is a simple annotated text document that has snippets of Imagej macros or Beanshell arranged into buttons. This tool is very useful when creating custom work flows integrating multiple components. Each component can be linked to a button for a more streamlined and accessible workflow.

## Features >The IJBlob library indentifying connected components in binary images. The algorithm used for connected component labeling is: >Chang, F. (2004). A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002 ##Reference Wagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. Journal of Open Research Software 1(1):e6, DOI:

An ImageJ plugin for selecting a plane in focus among multiple slices image stack. The algorithm uses normalized variance. A short tutorial is available in the plugin web page (above).

Segmentation of Golgi.

Sample Images can be found here.

OMERO is a free, open source image management software. It is client-server based system which supports 5D images, including big images and high-content screening data. Data are stored on a server using relational database. They are accessed using 3 main clients, a desktop client, a web client and a command line tool. There are bindings from OMERO to other image analysis packages, like FLIMfit, OMERO.searcher. The data in OMERO are organized in groups. A user can be a member of one or more groups. This groups can be collaborative or private, there are 4 levels of permissions to access/edit/annotate/delete the data of other users.

The package is supported not only by community forums, but also by a dedicated team which helps users to solve their problems and deals with the bugs submitted via error submission system.

###Strengths

Open source, scalable software, Supports diverse sets of imaging applications and domains (EM,LM, HCS, DigPath) Cross-platform, Java-based application, API support for Java, Python, C++, Django, On-line Forums, Automatic QA and upload of software errors Multi-dimensional images, Web access, Free Demo-server accounts

Limitations

Enterprise-scale software, so complex install, requires expertise, Actively developing API, Python scripts and functions still developing

‘’’Squassh’’’ is a tool for 2D and 3D segmentation and quantification of subcellular shapes in fluorescence microscopy images. It provides globally optimal detection and segmentation of objects with constant internal intensity distribution, followed by object-based colocalization analysis. The segmentation computed by Region Competition can optionally correct for the PSF of the microscope, hence providing optimally deconvolved segmentations. Part of the mosaic suite

Image segmentation based on the MOSAIC Discrete region competition algorithm. 

Easy-to-use, computationally efficient, two- and three-dimensional, feature point-tracking tool for the automated detection and analysis of particle trajectories as recorded by video imaging in cell biology. 

The tracking process requires no apriori mathematical modelling of the motion, it is self-initialising, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. 

The plugin is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. It allows the user to visualize and analyze the detected particles and found trajectories in various ways:

• Preview and save detected particles for separate analysis
• Global non progressive view on all trajectories
• Focused progressive view on individually selected trajectory
• Focused progressive view on trajectories in an area of interest

It also allows the user to find trajectories from uploaded particles position and information text files and then to plot particles parameters vs. time - along a trajectory

This ImageJ plugin creates high resolution PSF images by averaging many bead im- ages as well as exploiting the assumption that the point spread function is rotationally symmetric with respect to the axial axis (z-direction).

OMERO.webtagging is the umbrella name for tools developed to enhance use of text annotations (tags) in OMERO. There are two tools at present, autotag and tagsearch.

This plugin can be used for inferring spatial interactions between patterns of spot-like objects in images or between coordinates read from a file. 

This plugin can be used to add synthetic Poisson-distributed noise to an image in order to simulate shot noise of various signal-to-noise ratios. It can be used to generate benchmark images in order to assess the accuracy and robustness of image processing algorithms as a function of the noise level present in images.

Histogram-based background subtractor for ImageJ.

The implemented algorithm is based on the assumption that, compared to the background region, object (foreground) regions are small. The plugin builds local histograms and assumes the most occuring intensity to be part of the background.

EnhanceEdges enhances or identifies edges in an image, which can improve object identification or other downstream image processing.

This plugin threshold an image using the Maximum Entropy algorithm, which aims at maximizing the inter-class entropy. Entropy is defined as -sum(p.*log2(p)), where p contains the histogram bin counts. This thresholding is very useful to segment images with few bright objects on large dark background. In ImageJ/FIJI you can acces this tool in Image->Adjust->Threshold and choose in the list In Aphelion, you can access this tool in Seglmentation->Threshold-> AphImgEntropyThreshold

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This plugin allows to apply a free affine transformation to a 2D image in an interactive way.

The purpose of this plugin is to register—in other words, to align or to match—two images, one of them being called the source image and the other the target image.

This plugin provides a painter to visualize 2D flows. 2D Flows are couples of two sequences, one for the horizontal displacements, the other for the vertical displacements. This plugin provides a painter that draws flow arrows on top of another sequence.

This plugin facilitates the assembly of a mosaic of overlapping individual images, or tiles. It provides a semi-automated solution where the initial rough positioning of the tiles must be performed by the user, and where the final delicate adjustments are performed by the plugin.

The MosaicJ plugin requires that a second plugin, named TurboReg, is installed. 

This plugin allows to analyze the local direction and frequency of sinusoidal waves, for example muscle repetitive stripy pattern.

The output are optionally smoothed lambda (inversely proportional to the frequency) and phi (direction in degrees).

This plugin performs Strahler analysis on topographic skeletons (2D/3D). Strahler numbering is a numerical procedure that summarizes the branching complexity of mathematical trees.

Three different methods for correcting fluorescence bleaching. 1. Simple (framewise ratio based) 2. Exponential (curve fitting with exponential decay model) 3. Histogram matching (register histogram shape. with 16 bit, it takes long time... it should be improved).

This plugin automatically threshold an image using the Mixture Modeling algorithm. It is an histogram-based technique that assumes that the histogram distribution is represented by two Gaussian curves.

View your multi-channel data as a montage with one image per channel plus a merged, color channel (useful to create figures for communication). 

This viewer plugin will split all N channels of the active sequence into a montage of N+1 images, with one image for each channel, plus an additionnal color (merge) rendering.

Image segmentation by representative colors selection. Two versions are available :

• thresholding
• positive and negative colors selection and SVM learning

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Coloc 2 is Fiji's plugin for colocalization analysis. It implements and performs the pixel intensity correlation over space methods of Pearson, Manders, Costes, Li and more, for scatterplots, analysis, automatic thresholding and statistical significance testing.

Coloc 2 does NOT perform object based colocalization measurements, where objects are first segmented from the image, then their spatial relationships like overlap etc. are measured. This complementary approach is implemented in many ways elsewhere.

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Released in 2007, there is a newer "remastered" version "DeconvolutionLab2" (2017)

Provides a selection of spatial, separable and customizable filters in 1D and 2D, with OpenCL implementation if supported.

This originally came from this module.

Currently it is available as the ilastik CellProfiler plugin (see this image.sc post for details).