OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 2 — Feb. 1, 2012
  • pp: 215–224

The efficacy of image correlation spectroscopy for characterization of the extracellular matrix

Sadiq Mohammed Mir, Brenda Baggett, and Urs Utzinger  »View Author Affiliations

Biomedical Optics Express, Vol. 3, Issue 2, pp. 215-224 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (3035 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Image correlation spectroscopy (ICS) is known to be a useful tool for the evaluation of fiber width in the extracellular matrix. Here we evaluate a more general from of ICS fit parameters for fiber networks and arrive at a means of quantifying the fiber density, pore size and length which facilitates the characterization of the extracellular matrix. A simulation package was made to create images with different structural properties of fiber networks such as fiber orientation, width, fiber density and length. A pore finding algorithm was developed which determines the distribution of circular voids in the image. Collagen I hydrogels were prepared under different polymerization conditions for validation of our pore size algorithm with microscopy data. ICS parameters included amplitude, standard deviation and ellipticity and are shown to predict the structural properties of fiber networks in a quantitative manner. While the fiber width is related to the ICS sigma; the fiber density relates to the pore size distribution which correlates with the ICS amplitude in thresholded images. Fiber length is related to ICS ellipticity if the fibers have a preferred orientation. Findings from ICS and pore distribution algorithms were verified for both simulated and microscopy data. Based on these findings, we conclude that ICS can be used in the assessment of the extracellular matrix and the prediction of fiber orientation, width, density, length and matrix pore size.

© 2012 OSA

OCIS Codes
(100.2960) Image processing : Image analysis
(180.4315) Microscopy : Nonlinear microscopy

ToC Category:
Image Processing

Original Manuscript: September 7, 2011
Revised Manuscript: November 23, 2011
Manuscript Accepted: December 2, 2011
Published: January 3, 2012

Sadiq Mohammed Mir, Brenda Baggett, and Urs Utzinger, "The efficacy of image correlation spectroscopy for characterization of the extracellular matrix," Biomed. Opt. Express 3, 215-224 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. A. Nohe and N. O. Petersen, “Image correlation spectroscopy,” Sci. STKE2007(417), pl7 (2007). [CrossRef] [PubMed]
  2. D. L. Kolin and P. W. Wiseman, “Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells,” Cell Biochem. Biophys.49(3), 141–164 (2007). [CrossRef] [PubMed]
  3. M. Srivastava and N. O. Petersen, “Diffusion of transferrin receptor clusters,” Biophys. Chem.75(3), 201–211 (1998). [CrossRef] [PubMed]
  4. P. W. Wiseman, J. A. Squier, M. H. Ellisman, and K. R. Wilson, “Two-photon image correlation spectroscopy and image cross-correlation spectroscopy,” J. Microsc.200(1), 14–25 (2000). [CrossRef] [PubMed]
  5. A. Nohe, E. Keating, T. M. Underhill, P. Knaus, and N. O. Petersen, “Dynamics and interaction of caveolin-1 isoforms with BMP-receptors,” J. Cell Sci.118(3), 643–650 (2005). [CrossRef] [PubMed]
  6. C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J.92(6), 2212–2222 (2007). [CrossRef] [PubMed]
  7. C. B. Raub, J. Unruh, V. Suresh, T. Krasieva, T. Lindmo, E. Gratton, B. J. Tromberg, and S. C. George, “Image correlation spectroscopy of multiphoton images correlates with collagen mechanical properties,” Biophys. J.94(6), 2361–2373 (2008). [CrossRef] [PubMed]
  8. P. Friedl and K. Wolf, “Plasticity of cell migration: a multiscale tuning model,” J. Cell Biol.188(1), 11–19 (2010). [CrossRef] [PubMed]
  9. F. Sabeh, R. Shimizu-Hirota, and S. J. Weiss, “Protease-dependent versus -independent cancer cell invasion programs: three-dimensional amoeboid movement revisited,” J. Cell Biol.185(1), 11–19 (2009). [CrossRef] [PubMed]
  10. B. A. Roeder, K. Kokini, J. E. Sturgis, J. P. Robinson, and S. L. Voytik-Harbin, “Tensile mechanical properties of three-dimensional type I collagen extracellular matrices with varied microstructure,” J. Biomech. Eng.124(2), 214–222 (2002). [CrossRef] [PubMed]
  11. D. Kolin, “ICS Tutorial” (Cell Migration Consortium, 2006), http://www.cellmigration.org/resource/imaging/imaging_resources.shtml#software .
  12. R. Lumia, “A new three-dimensional connected components algorithm,” Comput. Vis. Graph. Image Process.23(2), 207–217 (1983). [CrossRef]
  13. N. D. Kirkpatrick, S. Andreou, J. B. Hoying, and U. Utzinger, “Live imaging of collagen remodeling during angiogenesis,” Am. J. Physiol. Heart Circ. Physiol.292(6), H3198–H3206 (2007). [CrossRef] [PubMed]
  14. J. S. Lim, Two-Dimensional Signal and Image Processing, Prentice Hall Signal Processing Series (Prentice Hall, Englewood Cliffs, N.J., 1990), pp. xvi, 694.
  15. K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Graphics Gems IV (Academic Press Professional, 1994), pp. 474–485.
  16. N. Otsu, “Threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern.9(1), 62–66 (1979). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited