Mean square displacement (MSD) analysis is a technique commonly used in colloidal studies and biophysics to determine what is the mode of displacement of particles followed over time. In particular, it can help determine whether the particle is:

  • freely diffusing;
  • transported;
  • bound and limited in its movement.

On top of this, it can also derive an estimate of the parameters of the movement, such as the diffusion coefficient.

@msdanalyzer is a MATLAB per-value class that helps performing this kind of analysis. The user provides several trajectories he measured, and the class can derive meaningful quantities for the determination of the movement modality, assuming that all particles follow the same movement model and sample the same environment.

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Examples of tracks to perform MSD analysis.



FoCuS-scan is software for processing and analysis of large-scale scanning fluorescence correlation spectroscopy (FCS) data. FoCuS-scan can correlate data acquired on conventional turn-key confocal systems and in the form of xt image carpets.



Biocat is a java based software that allows to perform image classification or segmentation using machine learning. Several algorithm for the classification are available.

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Stochastic optical reconstruction microscopy (STORM) and related methods achieves sub-diffraction-limit image resolution through sequential activation and localization of individual fluorophores. The analysis of image data from these methods has typically been confined to the sparse activation regime where the density of activated fluorophores is sufficiently low such that there is minimal overlap between the images of adjacent emitters. Recently several methods have been reported for analyzing higher density data, allowing partial overlap between adjacent emitters. However, these methods have so far been limited to two-dimensional imaging, in which the point spread function (PSF) of each emitter is assumed to be identical.

In this work, we present a method to analyze high-density super-resolution data in three dimensions, where the images of individual fluorophores not only overlap, but also have varying PSFs that depend on the z positions of the fluorophores.


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R wrapper around the OMERO Java Gateway, to enable access to OMERO via R using rJava

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OpenImadis stands for Open Image Discovery: A platform for Image Life Cycle Management. It was previously called CID iManage (for Curie Image Database).

No image data conversions, no duplication.

- Uploads data to a secure server in the original format

- Unique id for data

Supports sharing and collaboration with access control

- Allows users to upload, view, update or download data based on their access privileges

Supports multiple ways of attaching meta-information

- Annotations, comments and file attachments

-Analysis results as query-able visual objects

Supports Archiving (data moving to another long-term storage but still searchable)

Facilitates custom visualization and analysis

- Access data from preferred analysis and visualization tools

- Access relevant bits of data to build efficient web and mobile application

Facilitate easy access to analysis and visualization applications hosted on other servers

- Run analysis on dedicated compute clusters

- Access applications hosted and published by other users

Highly Scalable

- Supports on-the-fly addition of server nodes to scale concurrent usage




Cell or particle counting and scoring the percentage of stained objects


This one example workflow from the Cell Profiler(CP)  Examples . CP is commonly used to count cells or other objects as well as percent-positives, by measuring the per-cell staining intensity. This pipeline shows how to do both of these tasks, and demonstrates how various modules may be used to accomplish the same result. 

In a few words, it used the IdentifyPrimaryObject module of CellProfiler to detect nuclei from a channel (e.g DAPI), then again the same module on another channel to detect another probe (e.g some particular histone)  .

Then objects (nuclei) are related to the second object (Histone), to create a parent child-relation ship: where nuclei can have histone has child. Nuclei are then filtered according to the property of having histone (positive) or not having histone (negtiveobject) related to them.  If needed, nuclei can be expanded in order to include touching object rather than object inside only.

The percentage of positive nuclei vs total number of nuclei can then be computed using the CalculateMath Module.


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 described in the paper itself, 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, including the bouding box. Then the analysis of diameters and the statistical test are performed using 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.

Detection of Molecules - DoM


A collection of components for super resolution image data:

  • Detect Molecules
  • Reconstruct Image
  • Results table
  • Drift correction
  • Chromatic correction