Subpixel Measurement of Living Skin Deformation Using Intrinsic Features

  • Amir HajiRassoulihaEmail author
  • Andrew J. Taberner
  • Martyn P. Nash
  • Poul M. F. Nielsen
Conference paper


Accurate measurement of skin deformation is essential to study and understand its behaviour under mechanical load. Digital image correlation (DIC) techniques are commonly used for in-vivo subpixel measurements of deformation using camera-based devices. However, most of the existing DIC methods have modest accuracy and require the addition of feature-rich textures in order to measure the deformations. These limitations have made it challenging to measure skin deformations using DIC, especially where the skin does not have a rich texture and high measurement accuracies are required. Recently, an accurate and robust algorithm, named phase-based Savitzky–Golay gradient correlation (P-SG-GC), has been proposed for subpixel image registration. This algorithm addresses many of the limitations of existing DIC algorithms, and its advantages could lead to new advances in measuring skin deformations. In this paper, we test the accuracy and applicability of P-SG-GC for measuring subpixel deformations of living skin.

Experiments were performed using a camera, and a flat object attached to a linear translational stage. A series of translational shifts were applied to the object using the linear stage and were measured by P-SG-GC. The result showed that P-SG-GC could successfully estimate translational shifts ranging from 0.05 pixel to larger than 20 pixels (physical shifts from 5 to 2000 μm) in a subimage of 64 × 64 pixel. The standard deviations of the measurements for translational shifts ranged from 0.008 pixel to a maximum of 0.045 pixel in the camera images (i.e. 0.8–4.5 μm). The P-SG-GC algorithm was then used to measure skin deformation over an approximately 100 × 100 mm field-of-view. Results showed that P-SG-GC was capable of measuring skin deformations ranging from subpixel values to more than 19 pixels using only the intrinsic features of skin. The results illustrate that P-SG-GC is a robust, efficient, and accurate algorithm that can significantly improve the methods of measuring deformation distributions of living skin.


Skin In-vivo Subpixel deformation measurement Soft tissue Intrinsic features 


  1. 1.
    Junker JPE, Philip J, Kiwanuka E, Hackl F, Caterson EJ, Eriksson E (2014) Assessing quality of healing in skin: review of available methods and devices. Wound Repair Regen 22(Suppl 1):2–10CrossRefGoogle Scholar
  2. 2.
    Pawlaczyk M, Lelonkiewicz M, Wieczorowski M (2013) Age-dependent biomechanical properties of the skin. Postpy Dermatol Alergol 30:302–306Google Scholar
  3. 3.
    Miura N, Arikawa S, Yoneyama S, Koike M, Murakami M, Tanno O (2012) Digital image correlation strain analysis for the study of wrinkle formation on facial skin. J Solid Mech Mater Eng 6:545–554CrossRefGoogle Scholar
  4. 4.
    Flynn C, Taberner A, Nielsen P (2011) Modeling the mechanical response of in vivo human skin under a rich set of deformations. Ann Biomed Eng 39:1935–1946CrossRefGoogle Scholar
  5. 5.
    Jor JWY, Parker MD, Taberner AJ, Nash MP, Nielsen PMF (2013) Computational and experimental characterization of skin mechanics: identifying current challenges and future directions. Wiley Interdiscip Rev Syst Biol Med 5:539–556CrossRefGoogle Scholar
  6. 6.
    Kvistedal YA, Nielsen PMF (2009) Estimating material parameters of human skin in vivo. Biomech Model Mechanobiol 8:1–8CrossRefGoogle Scholar
  7. 7.
    Li J, Liu H, Althoefer K, Seneviratne LD (2012) A stiffness probe based on force and vision sensing for soft tissue diagnosis. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 1:944–947Google Scholar
  8. 8.
    Diridollou S, Patat F, Gens F, Vaillant L, Black D, Lagarde JM, Gall Y, Berson M (2000) In vivo model of the mechanical properties of the human skin under suction. Skin Res Technol 6:214–221CrossRefGoogle Scholar
  9. 9.
    Pailler-Mattei C, Bec S, Zahouani H (2008) In vivo measurements of the elastic mechanical properties of human skin by indentation tests. Med Eng Phys 30:599–606CrossRefGoogle Scholar
  10. 10.
    Fan Z, Samuel Q, Ilana BS, William N, Allison RP (2014) Sensory substitution using 3-Degree-of-Freedom tangential and normal skin deformation feedback. 2014 IEEE Haptics Symposium. 1–1Google Scholar
  11. 11.
    Flynn C, Taberner AJ, Nielsen PMF, Fels S (2013) Simulating the three-dimensional deformation of in vivo facial skin. J Mech Behav Biomed Mater 28:484–494CrossRefGoogle Scholar
  12. 12.
    Mahmud J, Evans SL, Holt CA (2012) An innovative tool to measure human skin strain distribution in vivo using motion capture and delaunay mesh. J Mech 28:309–317CrossRefGoogle Scholar
  13. 13.
    Sasaki A, Hashimoto H (2013) Measurement of hand skin deformation in dexterous manipulation. In: IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society, pp 8306–8311Google Scholar
  14. 14.
    Kacenjar S, Chen S, Jafri M, Wall B, Pedersen R, Bezozo R (2013) Near real-time skin deformation mapping. SPIE-IS&T 8655, pp 86550G-1–86550G-14Google Scholar
  15. 15.
    Tepole AB, Gart M, Gosain AK, Kuhl E (2014) Characterization of living skin using multi-view stereo and isogeometric analysis. Acta Biomater 10:4822–4831CrossRefGoogle Scholar
  16. 16.
    Hajirassouliha A, Taberner AJ, Nash MP, Nielsen PMF Subpixel phase-based image registration using Savitzky-Golay differentiators in gradient-correlation. IEEE Trans Image Process (Under Review)Google Scholar
  17. 17.
    Pan B, Qian K, Xie H, Asundi A (2009) Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Meas Sci Technol 20:62001CrossRefGoogle Scholar
  18. 18.
    Hajirassouliha A, Taberner AJ, Nash MP, Nielsen PMF Subpixel Phase-based image registration using Savitzky-Golay differentiators in gradient-correlation. IEEE Trans Image Process (Under Review) Google Scholar
  19. 19.
    Wöhler C (2013) 3D computer vision: efficient methods and applications. Springer, London, p 5CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Amir HajiRassouliha
    • 1
    Email author
  • Andrew J. Taberner
    • 1
    • 2
  • Martyn P. Nash
    • 1
    • 2
  • Poul M. F. Nielsen
    • 1
    • 2
  1. 1.Auckland Bioengineering InstituteThe University of AucklandAucklandNew Zealand
  2. 2.Department of Engineering ScienceThe University of AucklandAucklandNew Zealand

Personalised recommendations