ImFCS ImageJ Plugin

Imaging_FCS 1.491 is a basic ImageJ plugin to calculate and view spatio-temporal correlation functions from 16 bit gray tiff stack files. It was written under FIJI (ImageJ 1.51f; Java 1.8.0_102) and requires Imagescience for statistics (simulator) and Apache Poi for file reading and writing.

Imaging FCS 1.491 tries to provide a comprehensive software tool to calculate and evaluate spatiotemporal correlation functions. It includes the calculation of all auto- or cross-correlation functions for arbitrary pixel binning and regions of interest within an image, provides fit functions for total internal reflection fluorescence (TIRF) and single plane illumination microscopy (SPIM) based FCS measurements, can calculate the FCS diffusion laws and contains a basic simulator to create simulated data for different diffusive modes.

Imaging_FCS runs under ImageJ, FIJI and Micromanager, and it runs on PC as well as on Mac OS. We will use in the following text always FIJI but it should be understood that the same is true for ImageJ and Micromanager.

Imaging FCS 1.491 has a bug fixed from its earlier version of 1.49 that prevented saving the results under some circumstances.

Currently we provide two ways to install the plugin.

Option 1: By using the Update sites of ImageJ/Fiji

Click on Help -> Update. Later click on Manage update sites. Check the boxes next to ImagingFCS and ImageScience. Click on Close. Now click on the Apply Changes button. The plugin will be downloaded. As instructed, please restart ImageJ. The plugin can now be used.

Option 2: By downloading the files provided here.

The following files are needed:

Imaging_FCS_1_491.jar: Put this file in the plugin folder of FIJI (“\plugins”) and start FIJI. You will find Imaging_FCS_1_491 under the plugin tab.

Imagescience: Either install imagescience.jar in the jar folder within FIJI or link the update side to imagescience. This supports the probability distributions used in the simulator.

Apache POI:  You need to install Apache poi-3.17 (the latest stable release at the time of writing). The Apache Poi provides the necessary code for the writing and reading of .xlsx spreadsheet files, which are used to store and read experimental data. You can copy the whole poi-3.17 folder into the jars folder of Fiji (\\jars). It has also been found that sometimes there are errors in reading the jar files inside the poi folder.

In case, if there are errors while trying to run the plugin and if the error is associated with poi files, one suggestion is to place all the jar files inside the poi folder directly under the jars folder. In total, there must be 13 jar files as per poi 3.17. Six of them are found just inside the folder. Five of them are in the lib folder and another two of them are in the ooxml-lib folder.

If you want to compile the program yourself:

You will then need : This is the Java code from which the .jar file was produced. You can open it in FIJI and compile it yourself.

PC: If you want to compile the program on a PC in ImageJ, you need to install a JDK (e.g. I used Jave SE - jdk1.8.0_102). For details see and

Mac: At the moment Fiji cannot compile java code on Mac OS. So if you want to compile the program yourself you need to install a Java IDE, i.e. an Integrative Development Environment. Netbeans, for instance, is free and worked fine for us. But there are other free IDEs, e.g. Eclipse, JSource etc.

An important note

Switch off SCIFIO for file opening in ImageJ2 as Imaging_FCS_1_491 does not work with SCIFIO yet. To switch SCIFIO off, go to Edit\Options\ImageJ2 in the Fiji control bar. A dialog will appear in which you can untick the option “Use SCIFIO when opening files”.

Example File

Bilayer.tif : This is an example tiff stack to test the program. In general a tiff stack should contain at least 20,000 frames which were recorded with a time resolution of 1 ms or less. This is sufficient to resolve the dynamics in lipid bilayers. However, we recommend to take at least 50,000 frames for better statistics. For faster processes, shorter frame times and more frames are required (see Sankaran et al. Analytical Chemistry 2013).

Some time scales

Assuming a 21x21 pixel image with 50,000 frames then here are some characteristic times the program needs for the data evaluation (based on a PC Intel i7-3770 3.4 GHz, 64 bit, 16 GB RAM):

Calculation of all autocorrelation functions (ACFs): 22 s
Calculation of all ACFs and data fitting with a weighted NLS fit: 35 s
Calculation of all ACFs and data fitting with a NLS fit plus Bayes model selection (2 models): 13 min
Calculation of all ACFs and data fitting with a GLS fit: 43 s
Calculation of all ACFs and data fitting with a GLS fit plus Bayes model selection (2 models): 8 min
A dCCF image takes about 50 s
The calculation of the Imaging FCS diffusion law plot (5 points, no overlap) takes about 67 s
The PSF calibration takes about 5 minutes

Bleach corrections add between 2-20 s seconds to the overall calculation time.

The long times for Bayes model selection are partly due to some non-convergent fits if a model does not fit the data well. However, for application of Bayes to single pixels the results are almost instantaneous.

Imaging FCS_1_49 Manual: This manual contains the basic instructions how to use the program, the definition of all items in the control and fit panels, the file formats of the saved data, and the theoretical functions used for fitting.

Coming from the C programming language, the change to Java was or rather is cumbersome. To get an idea of this culture shock, see "The kingdom of nouns" (


Correlator Scheme

Meseth, U., T. Wohland, R. Rigler, and H. Vogel. 1999. Resolution of fluorescence correlation measurements. Biophys J. 76: 1619–1631.

Shi, X., and T. Wohland. 2010. Fluorescence Correlation Spectroscopy. In: Diaspro A, editor. Nanoscopy and Multidimensional Optical Fluorescence Microscopy. Boca Raton: CRC Press. pp. 6.1–6.34.

Sankaran, J., X. Shi, L.Y. Ho, E.H.K. Stelzer, and T. Wohland. 2010. ImFCS: a software for imaging FCS data analysis and visualization. Opt Express. 18: 25468–25481.

Imaging FCS publications

Kannan, B., J. Har, P. Liu, I. Maruyama, J. Ding, et al. 2006. Electron multiplying charge-coupled device camera based fluorescence correlation spectroscopy. Anal Chem. 78: 3444–3451.

Kannan, B., L. Guo, T. Sudhaharan, S. Ahmed, I. Maruyama, et al. 2007. Spatially resolved total internal reflection fluorescence correlation microscopy using an electron multiplying charge-coupled device camera. Anal Chem. 79: 4463–4470.

Guo, L., J.Y. Har, J. Sankaran, Y. Hong, B. Kannan, et al. 2008. Molecular Diffusion Measurement in Lipid Bilayers over Wide Concentration Ranges: A Comparative Study. ChemPhysChem. 9: 721–728.

Sankaran, J., M. Manna, L. Guo, R. Kraut, and T. Wohland. 2009. Diffusion, Transport, and Cell Membrane Organization Investigated by Imaging Fluorescence Cross-Correlation Spectroscopy. Biophys J. 97: 2630–2639.

Wohland, T., X. Shi, J. Sankaran, and E. Stelzer. 2010. Single plane illumination fluorescence correlation spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments. Opt Express. 18: 10627–10641.

Kraut, R., N. Bag, and T. Wohland. 2012. Methods in Cell Biology. Elsevier.

Bag, N., J. Sankaran, A. Paul, R.S. Kraut, and T. Wohland. 2012. Calibration and Limits of Camera-Based Fluorescence Correlation Spectroscopy: A Supported Lipid Bilayer Study. ChemPhysChem. 13: 2784–2794.

Sankaran, J., N. Bag, R.S. Kraut, and T. Wohland. 2013. Accuracy and Precision in Camera-Based Fluorescence Correlation Spectroscopy Measurements. Anal Chem. : 130404142328008.

Bag, N., A. Ali, V.S. Chauhan, T. Wohland, and A. Mishra. 2013. Membrane destabilization by monomeric hIAPP observed by imaging fluorescence correlation spectroscopy. Chem. Commun. 49: 9155.

Singh, A.P., J.W. Krieger, J. Buchholz, E. Charbon, J. Langowski, et al. 2013. The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy. Opt Express. 21: 8652.

Bag N., D.H.X. Yap, T. Wohland, “Temperature dependence of diffusion in model and live cell membranes characterized by imaging fluorescence correlation spectroscopy”. BBA Biomembranes 1838 (2014) 802–813.

Guo, S.M., N. Bag, A. Mishra, T. Wohland, and M. Bathe, “Bayesian Total Internal Reflection Fluorescence Correlation Spectroscopy Reveals hIAPP-Induced Plasma Membrane Domain Organization in Live Cells”, Biophy. J. 2014, (106) 190–200.

Krieger, J.W., A.P. Singh, C.S. Garbe, T. Wohland and J. Langowski, “Dual-Color Fluorescence Cross-Correlation Spectroscopy on a Single Plane Illumination Microscope (SPIM-FCCS)”, Optics Express (2014), 22(3), 2358–2375.

Bag, N; Huang, S; Wohland, T. “Plasma Membrane Organization of Epidermal Growth Factor Receptor in Resting and Ligand-Bound States”, Biophys J. 2015; 109(9):1925-36.

Machan R, Foo YH, Wohland T. On the Equivalence of FCS and FRAP: Simultaneous Lipid Membrane Measurements, Biophys. J. 2016, (111) 152–161.

Bag N, Ng XW, Sankaran J, Wohland T. Spatiotemporal mapping of diffusion dynamics and organization in plasma membranes. Methods Appl. Fluoresc. 4 (2016) 034003.

Ng XW, Teh C., Korzh V, and Wohland T. The Secreted Signaling Protein Wnt3 Is Associated with Membrane Domains In Vivo: A SPIM-FCS Study. Biophy.J. 2016, (111) 418–429.

Imaging FCS reviews

N. Bag and T. Wohland, “Imaging Fluorescence Fluctuation Spectroscopy: New Tools for Quantitative Bioimaging”, Ann. Rev. Phys. Chem. (2014) 65:225–248.

A.P. Singh and T. Wohland, “Applications of imaging fluorescence correlation spectroscopy”, Curr. Op. Chem. Biol. (2014) 20:29–35.


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