ImageJ Macros

Protein array is used to analyze protein expressions by screening simultaneously several protein-molecule interactions such as protein-protein and protein-DNA interactions. In most cases, the detection of interactions leads to an image containing numerous lines of spots that will be analyzed by comparing tables of intensity values. To describe the observed different patterns of expression, users generally show histograms with the original associated images [1]. The “Protein Array Analyzer” gives a friendly way to exploit this type of analysis, thus allowing quantification, image modeling and comparative analysis of patterns.

The Protein Array Analyzer, which was programmed in ImageJ’s macro language, is an extention of the Dot Blot Analyzer, [2], [3] a graphically interfaced tool that greatly simplifying analysis of dot arrays.

This workflow detects spots from a 3D image by using straightforward set of ImageJ components. It receives the Laplacian Radius and the Threshold  value s input.

This workflow detects spots in a 2D image by filtering the image by Laplacian of Gaussian (user defined radius) and detecting regional intensity minima (user defined noise tolerance).

The macro will segment nuclei and separate clustered nuclei in a 3D image using a distance transform watershed. As a result an index-mask image is written for each input image.

The macro will segment nuclei and separate clustered nuclei using a binary watershed. As a result an index-mask image is written for each input image.

CLIJ2 is a GPU-accelerated image processing library for ImageJ/Fiji, Icy, Matlab and Java. It comes with hundreds of operations for filtering, binarizing, labeling, measuring in images, projections, transformations and mathematical operations for images. While most of these are classical image processing operations, CLIJ2 also allows performing operations on matrices potentially representing neighborhood relationships between cells and pixels.

CLIJ2 was developed to process images from fluorescence microscopy data of developing cells, tissues, organoids and organisms.

InspectJ is a free ImageJ/FIJI tool to inspect digital image integrity.

InspectJ_v2 is a newer version for advanced users. It applies additional features like histogram equalization and gamma correction for improved image inspections.

This is a classical workflow for spot detection or blob like structures (vesicules, melanosomes,...)

Step 1 Laplacian of Gaussian to enhance spots . Paraeters= radius, about the average spot radius

Step 2 Detect minima (using Find Maxima with light background option to get minima). Parameter : Tolerance to Noise: to be tested, hard to predict. About the height of the enhanced feautures peaks

This script includes a rough feature detection and then fine 2D Gaussian algorithm to fit Gaussians within detected regions. This macro is unique because the ImageJ/Fiji curve fitting API only supports 1-D curve. I get around this by linearising the equation. This implementation is for isotropic (spherical) or anistropic (longer in x/y) diagonally covariant Gaussians but not fully covariant Gaussians (anisotropic and rotated). 

HyphaTracker propose a workflow for time-resolved analysis of conidia germination. Each part of this workflow can also be used independnatly , as a toolbox. It has been tested on bright-field microscopic images of conidial germination. Its purpose is mainly to identify the germlings and to remove crossing hyphae, and measure the dynamics of their growth.

The purpose of the workflow is ....

First you need

"The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length."[...]" NeuriteTracer is a fast simple-to-use ImageJ plugin for the analysis of outgrowth in two-dimensional fluorescence microscopy images of neuronal cultures. The plugin performed well on images from three different types of neurons with distinct morphologies."

This plugin requires parameter setting: Threshold levels and scale (see more details on the related publication)

The Adipocytes Tools help to analyze fat cells in images from histological section. This is a rather general cell segmentation approach. It can be adapted to different situations via the parameters. This means that you have to find the right parameters for your application.

Sample Image: [0178_x5_3.tif](http://dev.mri.cnrs.fr/attachments/190/0178_x5_3.tif)

Neural Circuit Tracer (NCTracer) is open source software for automated and manual tracing of neurites from light microscopy stacks of images. NCTracer has more than one workflow available for neuron tracing. 

"The Neural Circuit Tracer is open source software built using Java (Sun Microsystems) and Matlab (MathWorks, Inc., Natick MA). It is based on the core of ImageJ (http://rsbweb.nih.gov/ij) and the graphic user interface has been developed by using Java Swings. The software combines anumber of functionalities of ImageJ with several newly developed functions for automated and manual tracing of neurites. The Neural Circuit Tracer is designed in a way
that will allow the users to add any plug-ins developed for ImageJ. More importantly, functions written in MatLab and converted into Java with Matlab JA toolbox can also be added to the Neural Circuit Tracer." 

This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:

-Cytofluorogram

Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

Bio Image Analysis tool from REF

ImageJ macro for the morphometry of neurites. > NeurphologyJ; it is capable of automatically quantifying neuronal morphology such as soma number and size, neurite length, neurite ending points and attachment points. NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image-processing and analysis platform.

Estimate the frequency of hepatitis C virus infected cells based on the intensity of viral antigen associated immunofluorescence. 

The core is an ImageJ Macro, so it's easy to modify for one's own needs (Link to the code). 

In this workflow, you can use MorphoLibJ to generate accurate morphometric measurements

• First the fibers are segmented by mathematical morphology:
  • for example by using MorphoLibJ:
    • Create a marker image by creating a rough mask with extended regional maxima (similar to Find Max), such that you have one max per fiber
    • Use the marker controlled watershed (in MorpholLibJ/ Segmentation/ marker controlled watershed) : indicate the original grayscale image as the input, Marker will be your maxima image, select None for mask
    • it will create a label mask of your fibers
•  In MorphoLibJ /analyze/ select Region Morphometry: this will compute different shape factors which are more robust than the ones implemented by default in ImageJ
• Export the result table created to a csv file
• Then for example in Matlab or R, you can apply a PCA analysis (Principal component analysis) followed by a k-means with the number of class (clusters) (different fibers type) you want to separate.
• You can then add this class as a new feature to your csv file.
• From this you can sort your labelled fibers into these clusters for a visual feedback or further spatial analysis