Colocalization analysis
Co-localisation analysis



Computer-assisted Evaluation of Myelin formation (CEM) is a collection designed to automate myelin quantification. It requires use input to choose the best threshold values. The myelin is calculated as an overlap between neuronal signal and oligodendrocyte signal. Results are given as pixel counts and percents.

CEM runs as an imageJ plugin with an optional Matlab extension to remove cell bodies. More details are published at Kerman et al. 2015 Development. Supplemental Material includes a detailed user manual and the download link.




The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

GDSC plugins


Quote: "The GDSC ImageJ plugins are a collection of analysis programs for microscopy images including colocalisation analysis and peak finding (FindFoci)."



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:


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)

example of partial colocalisation from reference publication

Lama: The LocAlization Microscopy Analyzer


LocAlization Microscopy Analyzer (LAMA) is a software tool that contains several well-established data post processing algorithms for single-molecule localization microscopy (SMLM) data. LAMA has implemented algorithms for cluster analysis, colocalization analysis, localization precision estimation and image registration. LAMA works with a graphical user interface (GUI), and accepts simple input data formats as supported by various single- molecule localization software tools.

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Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh


A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin Squassh is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

2-D Colocalisation in Cells


The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. Please read here for more information:…


Input data type: 


Output data type: 

processed images, numbers, text file, csv files

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3D object based colocalization using KNIME


These two similar KNIME workflow solutions take 3D data stacks to segment the spots first, using local thresholding with subsequent morphological operations in order to remove noise. Colocalization is then defined by overlapping or center point distance between segmented objects. Further filtering such as overlapping ratio or distance range is done through KNIME table processing.

Two different types are available. 

  1. colocalization based on overlapping
  2. colocalization based on distance between object centers

Sample images: Smapp_Ori files

Chapter 4 in the documentation. 

Leaf Infection Tools


The Leaf Infection Tools allow to measure the area of leaves, of two stainings in different channels and of the overlap region of the two stainings. 


Test image:

a leaf with infection pattern