Skip to main content
SpringerLink
Log in

Polarized Image Correlation for Large Deformation Fiber Kinematics

  • Published:
Experimental Mechanics Aims and scope Submit manuscript

Abstract

The underlying fiber architecture of soft tissues, like bat wing skin, plays an important role in the material’s overall mechanical behavior. The mesoscopic birefringent fiber architecture of the bat wing skin can be visualized directly under polarized light. This inherent property is the key to the new experimental technique developed in this work: polarized image correlation (PIC). PIC is a technique for determining full field material strains and fiber kinematics of mesoscopically resolved fibrous tissues during biaxial mechanical testing. Not only is the material birefringence used to determine fiber kinematics under finite deformations, but it is also used for image correlation and strain field computation. Pure translation tests performed with PIC verify the accuracy of the technique. A segmental image processing method was developed to solve an experimental issue of changing birefringent properties to construct accurate continuous deformation profiles. By integrating PIC with traditional digital image correlation, both surface and subsurface data give additional insight into through thickness tissue behavior. The PIC technique is applicable to semi-transparent tissues with birefringent mesosopic structures; incorporation of microscopy would resolve smaller fiber structures. Future work includes extending the techniques to three dimensions to analyze curved surfaces.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. To fully characterize these materials, independent control of three separate strain components (or stress components) is required [38]. This would require independent control of, for example, the in-plane shear strains in addition to the two axial strains (see Holzapfel and Ogden [38] for a comprehensive discussion of the experimental requirements for constitutive modeling.). For thin biological membranes, biaxial testing may be sufficient since 2D membrane models are often applied.

  2. Clamping is another attachment option; however, in addition to potentially damaging the sample, clamping induces stress concentrations at the corners between clamps, effectively ‘stress-shielding’ the central region [39]. This stress-shielding phenomenon makes the tissue appear stiffer because the applied force is not fully transferred to the central region. In contrast, suturing methods allow the tissue to freely expand in the lateral direction, minimizing boundary effects [39].

References

  1. Vaughan TA (1966) Morphology and flight characteristics of molossid bats. J Mammal 249–260

  2. Hubel TY, Hristov NI, Swartz SM, Breuer KS (2010) Wake structure and wing kinematics: the flight of the lesser dog-faced fruit bat, Cynopterus brachyotis. J Exp Biol 213(20):3427–3440

    Article  Google Scholar 

  3. Swartz SM, Groves MS, Kim HD, Walsh WR (1996) Mechanical properties of bat wing membrane skin. J Zool 239(2):357–378

    Article  Google Scholar 

  4. Holbrook KA, Odland GF (1978) A collagen and elastic network in the wing of the bat. J Anat 126(Pt 1):21

    Google Scholar 

  5. Komatsu K, Chiba M (2001) Synchronous recording of load-deformation behaviour and polarized light-microscopic images of the rabbit incisor periodontal ligament during tensile loading. Arch Oral Biol 46(10):929–937

    Article  Google Scholar 

  6. Tower TT, Neidert MR, Tranquillo RT (2002) Fiber alignment imaging during mechanical testing of soft tissues. Ann Biomed Eng 30(10):1221–1233

    Article  Google Scholar 

  7. Komatsu K, Viidik A (1996) Changes in the fibre arrangement of the rat incisor periodontal ligament in relation to various loading levels in vitro. Achr Oral Biol 41(2):147–159

    Article  Google Scholar 

  8. Tower TT, Tranquillo RT (2001) Alignment maps of tissues: II. Fast harmonic analysis for imaging. Biophys 81(5):2964–2971

    Article  Google Scholar 

  9. Wang YN, Galiotis C, Bader DL (2000) Determination of molecular changes in soft tissues under strain using laser Raman microscopy. J Biomech 33(4):483–486

    Article  Google Scholar 

  10. Voytik-Harbin SL, Roeder BA, Sturgis JE, Kokini K, Robinson JP (2003) Simultaneous mechanical loading and confocal reflection microscopy for three-dimensional microbiomechanical analysis of biomaterials and tissue constructs. Microsc Microanal 9(01):74–85

    Article  Google Scholar 

  11. Hansen KA, Weiss JA, Barton JK (2002) Recruitment of tendon crimp with applied tensile strain. J Biomech Eng 124:72

    Article  Google Scholar 

  12. Sacks MS, Smith DB, Hiester ED (1997) A small angle light scattering device for planar connective tissue microstructural analysis. Ann Biomed Eng 25(4):678–689

    Article  Google Scholar 

  13. Gilbert TW, Sacks MS, Grashow JS, Savio LYW, Badylak SF, Chancello MB (2006) Fiber kinematics of small intestinal submucosa under biaxial and uniaxial stretch. J Biomech Eng 128:890

    Article  Google Scholar 

  14. Sacks MS, Sun W (2003) Multiaxial mechanical behavior of biological materials. Annu Rev Biomed Eng 5:251

    Article  Google Scholar 

  15. Lanir Y, Fung YC (1974) Two-dimensional mechanical properties of rabbit skin–I. Experimental system. J Biomech 7(1):29–34

    Article  Google Scholar 

  16. Yin FCP, Strumpf RK, Chew PH, Zeger SL (1987) Quantification of the mechanical properties of noncontracting canine myocardium under simultaneous biaxial loading. J Biomech 20(6):577–589

    Article  Google Scholar 

  17. Choi HS, Vito RP (1990) Two-dimensional stress–strain relationship for canine pericardium. J Biomech Eng 112(2):153–159

    Article  Google Scholar 

  18. Billiar KL, Sacks MS (2000) Biaxial mechanical properties of the natural and glutaraldehyde treated aortic valve cusp—Part I: Experimental results. J Biomech Eng 122:23

    Article  Google Scholar 

  19. Peters WH, Ranson WF (1982) Digital imaging techniques in experimental stress analysis. Opt Eng 21(3):213427

    Article  Google Scholar 

  20. Sutton MA, Wolters WJ, Peters WH, Ranson WF, McNeill SR (1983) Determination of displacements using an improved digital correlation method. Image Vision Comput 1(3):133–139

    Article  Google Scholar 

  21. Chu TC, Ranson WF, Sutton MA (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25(3):232–244

    Article  Google Scholar 

  22. Sacks MS (1999) A method for planar biaxial mechanical testing that includes in-plane shear. J Biomech Eng 121:551

    Article  Google Scholar 

  23. Zhang D, Eggleton CD, Arola DD (2002) Evaluating the mechanical behavior of arterial tissue using digital image correlation. Exp Mech 42(4):409–416

    Article  Google Scholar 

  24. Zhang D, Arola DD (2004) Applications of digital image correlation to biological tissues. J Biomed Opt 9:691

    Article  Google Scholar 

  25. Hoc T, Henry L, Verdier M, Aubry D, Sedel L, Meunier A (2006) Effect of microstructure on the mechanical properties of Haversian cortical bone. Bone 38(4):466–474

    Article  Google Scholar 

  26. Thompson MS, Schell H, Lienau J, Duda GN (2007) Digital image correlation: a technique for determining local mechanical conditions within early bone callus. Med Eng Phys 29(7):820–823

    Article  Google Scholar 

  27. Sutton MA, Ke X, Lessner SM, Goldbach M, Yost M, Zhao F, Schreier HW (2008) Strain field measurements on mouse carotid arteries using microscopic three-dimensional digital image correlation. J Biomed Mater Res A 84(1):178–190

    Article  Google Scholar 

  28. Gasser TC, Ogden RW, Holzapfel GA (2006) Hyperelastic modeling of arterial layers with distributed collagen fibre orientations. J R Soc Interface 3(6):15–35

    Article  Google Scholar 

  29. Wang Y, Son S, Swartz SM, Goulbourne NC (2012) A mixed von Mises distribution for modeling soft biological tissues with two distributed fiber properties. Int J Solids Struct 49(21):2914–2923

    Article  Google Scholar 

  30. Schreier HW, Braasch JR, Sutton MA (2000) Systematic errors in digital image correlation caused by intensity interpolation. Opt Eng 39:2915

    Article  Google Scholar 

  31. Schreier HW, Sutton MA (2002) Systematic errors in digital image correlation due to undermatched subset shape functions. ExpMech 42(3):303–310

    Google Scholar 

  32. Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Van Hemelrijck D, Habraken AM (2006) Quality assessment of speckle patterns for digital image correlation. Opt Laser Eng 44(11):1132–1145

    Article  Google Scholar 

  33. 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 Laser Eng 48(4):469–477

    Article  Google Scholar 

  34. Yaofeng S, Pang JHL (2007) Study of optimal subset size in digital image correlation of speckle pattern images. Opt Laser Eng 45(9):967–974

    Article  Google Scholar 

  35. GOM optical measuring techniques (2009) Chapter D: Computation. In Aramis user manual - software, ver6.1. GOM, Braunschweig, Germany

  36. Romhányi G (1983) The characteristic three-banded birefringence of ligamentum nuchae elastic fibres indicates ordered micellar texture. Histochem Cell Biol 77(1):133–139

    Article  Google Scholar 

  37. Aaron BB, Gosline JM (1981) Elastin as a random–network elastomer: a mechanical and optical analysis of single elastin fibers. Biopolymers 20(6):1247–1260

    Article  Google Scholar 

  38. Holzapfel GA, Ogden RW (2009) On planar biaxial tests for anisotropic nonlinearly elastic solids. A continuum mechanical framework. Math Mech Solids 14(5):474

    Article  MATH  Google Scholar 

  39. Sun W, Sacks MS, Scott MJ (2005) Effects of boundary conditions on the estimation of the planar biaxial mechanical properties of soft tissues. J Biomech Eng 127:709

    Article  Google Scholar 

Download references

Acknowledgments

We acknowledge support of the Air Force Office of Scientific Research (Grant #F023809), the National Science Foundation (NSF IOS 1145549), and fellowship support from the Horace Rackham Graduate School of the University of Michigan.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, University of Michigan, FXB Building, 1320 Beal Avenue, Ann Arbor, MI, 48109, USA

    A. J. Skulborstad, Y. Wang, J. D. Davidson & N. C. Goulbourne

  2. Department of Ecology and Evolutionary Biology and School of Engineering, Brown University, Box G-B206, Providence, RI, 02912, USA

    S. M. Swartz

Authors
  1. A. J. Skulborstad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. D. Davidson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. M. Swartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. N. C. Goulbourne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. C. Goulbourne.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Skulborstad, A.J., Wang, Y., Davidson, J.D. et al. Polarized Image Correlation for Large Deformation Fiber Kinematics. Exp Mech 53, 1405–1413 (2013). https://doi.org/10.1007/s11340-013-9751-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11340-013-9751-4

Keywords

Search

Navigation