Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-19T00:34:11.566Z Has data issue: false hasContentIssue false
  • English
  • Français

Speckle Patterning of Soft Tissues for Strain Field Measurement Using Digital Image Correlation: Preliminary Quality Assessment of Patterns

Published online by Cambridge University Press:  23 December 2010

Jinfeng Ning ,
Vaughn G. Braxton ,
Ying Wang ,
Michael A. Sutton ,
Yanqing Wang  and
Susan M. Lessner
Jinfeng Ning
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Vaughn G. Braxton
Affiliation:
Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USA
Ying Wang
Affiliation:
Biomedical Engineering Program, University of South Carolina, Columbia, SC 29208, USA
Michael A. Sutton
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA Biomedical Engineering Program, University of South Carolina, Columbia, SC 29208, USA
Yanqing Wang
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Susan M. Lessner*
Affiliation:
Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USA Biomedical Engineering Program, University of South Carolina, Columbia, SC 29208, USA
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Methods for creating speckle patterns on mouse arteries for use in deformation and strain field measurements in a stereomicroscope digital image correlation (DIC) system are described. Both fluorescent microsphere binding and ethidium bromide (EB) nuclear staining were used to generate high contrast, random patterns on mouse carotid arteries. To quantify the quality of each pattern, several metrics are used including (a) histogram distribution for each intensity pattern and (b) pixel-level variance in intensity pattern noise. Results demonstrate that both approaches provide sufficient pattern contrast for use in image-based methods to measure deformations in soft tissue. While fluorescent nuclear staining generates higher pixel-level intensity noise, this method provides better overall pattern quality (greater spatial uniformity and broader histogram) for automated DIC analysis when used at the appropriate magnification. Using recently developed theoretical predictions, estimates for the standard deviation in image-correlation-based displacements due to the measured intensity pattern variance are presented for fluorescent microsphere binding and EB nuclear staining patterns. Results confirm that both patterning approaches provide relatively small standard deviation in displacement measurements and hence are appropriate for measurement of deformations in small artery specimens.

Keywords

digital image correlationpattern developmentpattern assessmentintensity pattern noisedisplacement error estimatesbiomechanicssoft tissuemicrospheresnuclear staining
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 17 , Issue 1 , February 2011 , pp. 81 - 90
Copyright
Copyright © Microscopy Society of America 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Franklin, W.A. & Locker, J.D. (1981). Ethidium bromide: A nucleic acid stain for tissue sections. J Histochem Cytochem 29, 572–576.CrossRefGoogle Scholar
Lecompte, D., Smits, A., Bossuyt, S., Sol, H., Vantomme, J., Hemelrijck, D.V. & Habraken, A.M. (2006). Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44, 1132–1145.CrossRefGoogle Scholar
Pan, B., Lu, Z. & Xie, H. (2010). Mean intensity gradient: An effective global parameter for quality assessment of the speckle patterns used in digital image correlation. Opt Lasers Eng 48, 469–477.CrossRefGoogle Scholar
Schreier, H.W., Braasch, J. & Sutton, M.A. (2000). Systematic errors in digital image correlation caused by intensity interpolation. Opt Eng 39, 2915–2921.CrossRefGoogle Scholar
Schreier, H.W., Garcia, D. & Sutton, M.A. (2004). Advances in stereo light microscopy. Exp Mech 44, 278–289.CrossRefGoogle Scholar
Sun, Z., Lyons, J.S. & McNeill, S.R. (1997). Measuring microscopic deformations with digital image correlation. Opt Lasers Eng 27, 409–428.CrossRefGoogle Scholar
Sutton, M.A., Ke, X., Lessner, S.M., Goldbach, M., Yost, M., Zhao, F. & Schreier, H.W. (2008). Strain field measurements on mouse carotid arteries using microscopic three-dimensional digital image correlation. J Biomed Mater Res A 84, 178–190.CrossRefGoogle ScholarPubMed
Sutton, M.A., Orteu, J. & Schreier, H.W. (2009). Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. New York: Springer.Google Scholar
Wang, Y.Q., Sutton, M.A., Bruck, H.A. & Schreier, H.W. (2009). Quantitative error assessment in pattern matching: Effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements. Strain 45, 160–178.CrossRefGoogle Scholar
Zhang, D. & Arola, D.D. (2004). Applications of digital image correlation to biological tissues. J Biomed Opt 9, 691–699.CrossRefGoogle ScholarPubMed
Zhang, D.S., Eggleton, C.D. & Arola, D.D. (2002). Evaluating the mechanical behavior of arterial tissue using digital image correlation. Exp Mech 42, 409–416.CrossRefGoogle Scholar