"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)



EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.


Grayscale granulometry

This imageJ/Fiji plugin provides an analysis of the granulometry inside an image by mathematical morphology. It has sevral option for the structuring element to be used, and the size domain to be tested. The output will be both a curve of the remaining content of the image against the growing size of the structuring element, and the corresponding results table that could be then exported. It can deal with grayscale images directly, no need to segment the image first. This plugin can then be used to compare different texture based on some statistical analysis of the produced curve (for exemple comparison of the geometrical means to discriminate 2 textures). It is macro recordable as well. Programming Language: java Processes: successive erosion, dilation, closing or opening -> ANALYSIS User skills: Life Scientist, developers, analysts
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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.  

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.

MorphoLibJ: using morphological reconstructions to isolate objects


When trying to isolate objects, one strategy might be to use regular morphological operations (opening/closing) to remove small objects that are not of interest. In case small objects are made of a large number of pixels, this operation might impair the remaining objects' contours. An alternative strategy might be to use morphological reconstruction. In short, seed is placed on the image, on objects, then conditional dilation is performed from those seeds.

Here is how to proceed, using MorphoLibJ:

  1. Open an image
  2. Use the multi-point selection tool and place seeds on objects of interest
  3. Create a new image of same size, black background
  4. Transfer the selection to the new image (Edit/Selection/Restore selection)
  5. Draw (make sure you're using white foreground) the multiple point selection
  6. Launch the Morphological reconstruction plugin: Plugins > MorphoLibJ > Morphological reconstruction
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Segmentation of Clustered Cells in MATLAB


A clear tutorial on how to write a MATLAB script to segment clustered cells.

The full script is downloadable near the bottom of the article. 

Marker-controlled Watershed

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.
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Morphological Segmentation

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.
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This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

==== Strengths

The major strengths are in speed and quality of documentation. Almost all of the functionality is implemented in for multiple dimensions. It can be used with other Python packages which provide additional functionality.

Mahotas and all packages on which it relies are open-source.