Object-based colocalization
Object-based co-localisation

3D object based colocalisation

Bioimage Analyst

A user comes to the Facility: “I’ve got a set of 2 channels 3D images where objects are overlapping. I think the overlap might not be the same from object to object. I would like to quantify the physical overlap and get a map of quantifications”. Your mission: write the appropriate macro, knowing a user might always change her/his mind, and ask for more… Ready to take on the challenge ?



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.



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

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

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

CellProfiler Examples - Colocalization



Measuring the colocalization between fluorescently labeled molecules is a widely used approach to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identification and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels.

Sample image is included in the download package.