Quantification of outer ring diameters of centriole or PCM proteins of cycling HeLa cells in interphase


This workflow can be ran with data from 3D-SIM showing the centrosomes in order to compare the distribution of diameters of rings (or toroids) of different proteins from the centrioles or the peri centriolar material. It aims to reproduce the results of the Nature Cell Biology Paper Subdiffraction imaging of centrosomes reveals higher-order organizational features of pericentriolar material  from the same data set but with a different analysis method.

It is slightly different from the methods descrobed in the paper it self, where the method was to work on a maximum intensity projection of a 3D-SIM stack, and then to fit circle to the centrioles to estimate the diameters of the toroids.

In this workflow, the images are read from the IDR , then process by thresholding (Maximum entropy auto thresholding with Image J), and processed by Analyze Particles  with different measurement sets, includig the bouding box. Then the analysis of diameters and the statistical test is performed under R. All the code and data sets are available, and in the case of this paper have shown a layered organisation of the proteins.

Combined view from Figure 1 Lawo et al.



hIPNAT (hIPNAT: Image Processing for NeuroAnatomy and Tree-like structures) is a set of tools for the analysis of images of neurons and other tree-like morphologies. It is written for ImageJ, the de facto standard in scientific image processing. It is available through the ImageJ Neuroanatomy update site.

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classification of hemp fibers based on morphological features



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
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hemp analysis



MorphoLibJ is a library of plugin for ImageJ with functionalities for image processing such as filtering, reconstructing, segmenting, etc... Tools are based on Mathematical morphology with more rigorous mathematical approach than in the standard tools of ImageJ in particular for surface (or perimeter) measurements which are usually based on voxel counting.  


Programming language: JAVA

Among features:

* Morphological operations :  Dilation, Erosion, Opening,  Closing , Top hat (white and black), Morphological gradient (aka Beucher Gradient), Morphological Laplacian, Morphological reconstruction, Maxima/Minima , Extended Maxima/Minima -Watershed (classic or controlled) -Image overlay -Image labelling -Geodesic diameter -Region Adjacency Graph -Granulometry curves, morphological image analysis.

User skill : Life scientist, developers, analysts

several steps of morphological segmentation of plant tissue using MorphoLibJ.

Artemia color analysis


The Artemia Tools help to calculate the normalized redness of Artemia in color images.

See: http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Artemia_Tools

Test images: http://biii.eu/node/1139

Artemia color analysis toolset



ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif

3D Objects Counter

  • Counts the number of 3D objects in a stack.
  • quantifies for each found object the following parameters:
    • 3D intensity related measurement (with possible redirection to an image with the actual intensity value to be measured, for example for two channels measurements)
    • Volume and shape factors measurements, surface etc...
  • generates results representations such as:
    • Objects' map;
    • Surface voxels' map;
    • Centroids' map;
    • Centres of masses' map.

As ImageJ's “Analyze Particles” function, 3D-OC also has a “redirect to” option, allowing one image to be taken as a mask to quantify intensity related parameters on a second image. But unlike the Analyze particle, it include a thresholding option, meaning that you can start from a gray level  stack, not necessarily a binary mask.

To use it, first set the list of measurements by editing 3D OC Options. Both (3D Object counter and 3D OC Options are now in the default Fiji "Analyze" menu.

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ROI Statistics


This tool compute measures on the ROIs of the chosen sequence, updates the measures live when ROIs are changed and allows to copy/paste the measures to 3rd-party sheet edition softwares. Measures include geometric (bounding box) and intensity information.

It can complement the default ICY built inROI table, where measurements such as volume meausirements, intensity measurements, ... are built in and can be exported as excel as well.

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