Automated palpation for breast tissue discrimination based on viscoelastic biomechanical properties

  • Mariko Tsukune
  • Yo Kobayashi
  • Tomoyuki Miyashita
  • G. Masakatsu Fujie
Original Article



Accurate, noninvasive methods are sought for breast tumor detection and diagnosis. In particular, a need for noninvasive techniques that measure both the nonlinear elastic and viscoelastic properties of breast tissue has been identified. For diagnostic purposes, it is important to select a nonlinear viscoelastic model with a small number of parameters that highly correlate with histological structure. However, the combination of conventional viscoelastic models with nonlinear elastic models requires a large number of parameters. A nonlinear viscoelastic model of breast tissue based on a simple equation with few parameters was developed and tested.


The nonlinear viscoelastic properties of soft tissues in porcine breast were measured experimentally using fresh ex vivo samples. Robotic palpation was used for measurements employed in a finite element model. These measurements were used to calculate nonlinear viscoelastic parameters for fat, fibroglandular breast parenchyma and muscle. The ability of these parameters to distinguish the tissue types was evaluated in a two-step statistical analysis that included Holm’s pairwise \(t\) test. The discrimination error rate of a set of parameters was evaluated by the Mahalanobis distance.


Ex vivo testing in porcine breast revealed significant differences in the nonlinear viscoelastic parameters among combinations of three tissue types. The discrimination error rate was low among all tested combinations of three tissue types.


Although tissue discrimination was not achieved using only a single nonlinear viscoelastic parameter, a set of four nonlinear viscoelastic parameters were able to reliably and accurately discriminate fat, breast fibroglandular tissue and muscle.


Breast tumor diagnosis Palpation Nonlinear viscoelastic parameter Dynamic viscoelastic test Creep test 



This work was supported in part by Grants for Excellent Graduate Schools, Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT), a Grant-in-Aid of Scientific Research from MEXT (No. 26750171), Institute of Advanced Active Aging Research in Waseda University, Japan, and the Cooperative Research Project of the Institute of Development, Aging and Cancer, Tohoku University, Japan. This work received guidance from T. Hoshi (Waseda Univ., Japan), Y. Shiraishi (Tohoku Univ., Japan), T. Yambe (Tohoku Univ., Japan) and M. Hashizume (Kyushu Univ., Japan).

Conflict of interest

M. Tsukune, Y. Kobayashi, T. Miyashita and M. G. Fujie declare that they have no conflict of interest.


  1. 1.
    Porter PL (2009) Global trends in breast cancer incidence and mortality. Salud Publica Mex 51(supply 2):S141–S146CrossRefPubMedGoogle Scholar
  2. 2.
    Ophir J, Cespedes I, Ponnekanti H, Yazdi Y, Li X (1991) Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 13(2):111–134CrossRefPubMedGoogle Scholar
  3. 3.
    Cespedes I, Ophir J, Ponnekanti H, Maklad N (1993) Elastography: elasticity imaging using ultrasound with application to muscle and breast in vivo. Ultrason Imaging 15(2):73–88CrossRefPubMedGoogle Scholar
  4. 4.
    Garra BS, Cespedes EI, Ophir J, Spratt SR, Zuurbier RA, Magnant CM, Pennanen MF (1997) Elastography of breast lesions: initial clinical results. Radiology 202(1):79–86CrossRefPubMedGoogle Scholar
  5. 5.
    Lorenz A, Ermert H, Sommerfeld H-J, Garcia-SchÃijrmann M, Senge T, Philip-pou S (2000) Ultrasound elastography of the prostate: an innovative technique for tumour-detection [ultraschall-elastographie der prostata: Ein neues verfahren fur die tumorerkennung]. Ultraschall in der Medizin 21(1):8–15CrossRefPubMedGoogle Scholar
  6. 6.
    Nitta N, Shiina T (2002) Estimation of nonlinear elasticity parameter of tissues by ultrasound. Jpn J Appl Phys 4:3572–3578CrossRefGoogle Scholar
  7. 7.
    Hall TJ, Oberai AA, Barbone PE, Sommer AM, Gokhale NH, Goenezen S, Jiang J (2009) Elastic nonlinearity imaging. In: 31st Annual international conference of the IEEE EMBS, pp 1967–1970Google Scholar
  8. 8.
    Oberai AA, Gokhale NH, Goenezen S, Barbone PE, Hall TJ, Sommer AM, Jiang J (2009) Linear and nonlinear elasticity imaging of soft tissue in vivo: demonstration of feasibility. Phys Med Biol 54:1191–1207CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    Mehrabian H, Campbell G, Samani A (2010) A constrained reconstruction technique of hyperelasticity parameters for breast cancer assessment. Phys Med Biol 55:7489–7508CrossRefPubMedGoogle Scholar
  10. 10.
    Asbach P, Klatt D, Hamhaber U, Braun J, Somasundaram R, Hamm B, Sack I (2008) Assessment of liver viscoelasticity using multifrequency MR elastography. Magn Reson 60:373–379Google Scholar
  11. 11.
    Sack I, Beierbach B, Wuerfel J, Klatt D, Hamhaber U, Papazoglou S, Martus P, Braun J (2009) The impact of aging and gender on brain viscoelasticity. NeuroImage 46(3):652–657CrossRefPubMedGoogle Scholar
  12. 12.
    Klatt D, Papazoglou S, Braun J, Sack I (2010) Viscoelasticity-based magnetic resonance elastography of skeletal muscle. Phys Med Biol 55:6445–6459CrossRefPubMedGoogle Scholar
  13. 13.
    Krouskop TA, Wheeler TM, Kallel F, Garra BS, Hall T (1998) Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging 20:260–274CrossRefPubMedGoogle Scholar
  14. 14.
    Wellman PS, Howe RD, Dalton E, Kern KA (1999) Breast tissue stiffness in compression is correlated to histological diagnosis. PhD dissertation, Harvard University, USAGoogle Scholar
  15. 15.
    Matsumura T, Umemoto T, Fujihara Y, Ueno E, Yamakawa M, Shiina T, Mitake T (2009) Measurement of elastic property of breast tissue for elasticity imaging. In: Proceedings of IEEE international ultrasonics symposium, pp 1451–1454Google Scholar
  16. 16.
    Samani A (2009) Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples. Inst Phys Eng Med 54:2557–2569Google Scholar
  17. 17.
    Sack I, Johrens K, Wurfel J, Braun J (2013) Structure-sensitive elastography: on the viscoelastic powerlaw behavior of in vivo human tissue in health and disease. Soft Matter 9:5672–5680CrossRefGoogle Scholar
  18. 18.
    Tsukune M, Kobayashi Y, Hoshi T, Miyashita T, Fujie MG (2011) Evaluation and comparison of the nonlinear elastic properties of the soft tissues of the breast. In: Proceeding of the 33rd annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC2011), pp 7405–7408Google Scholar
  19. 19.
    Tsukune M, Kobayashi Y, Hoshi T, Shiraishi Y, Yambe T, Miyashita T, Fujie MG (2012) Nonlinear reaction force analysis for characterization of breast tissues, computer aided surgery (7th Asian conference on computer aided surgery, Bangkok, Thailand, August 2011 proceedings), Proceedings in Information and Communications Technology (PICT), vol 3. Springer, Berlin, pp 125–134Google Scholar
  20. 20.
    Tsukune M, Hatano M, Kobayashi Y, Miyashita T, Fujie MG (2013) Boundary condition generating large strain on breast tumor for nonlinear elasticity estimation. In: Proceedings of 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC2013), pp 4863–4866Google Scholar
  21. 21.
    Fung Y (1993) Biomechanics: mechanical properties of living tissues, vol 12. Springer, BerlinCrossRefGoogle Scholar
  22. 22.
    Marchesseau S, Heimann T, Chatelin S, Willinger R, Delingette H (2010) Fast porous visco-hyperelastic soft tissue model for surgery simulation: application to liver surgery. Prog Biophys Mol Biol 103(2–3):185–196CrossRefPubMedGoogle Scholar
  23. 23.
    Kobayashi Y, Kato A, Watanabe H, Hoshi T, Kawamura K, Okamoto J, Fujie MG (2012) Modeling of viscoelastic and nonlinear material properties of liver tissue using fractional calculation. J Biomech Sci Eng 7(2):177–187CrossRefGoogle Scholar
  24. 24.
    Robert B, Sinkus R, Larrat B, Tanter M, Fink M (2006) A new rheological model based on fractional derivatives for biological tissues. In: Proceeding of IEEE ultrasonics symposium, pp 1033–1036Google Scholar
  25. 25.
    Kelly JF, McGough RJ (2009) Fractal ladder models and power law wave equations. J Acoust Soc Am 126(4):2072–2081CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Kensinger RS, Collier RJ, Bazer FW (1986) Ultrastructural changes in porcine mammary tissue during lactogenesis. J Anat 145:49–59PubMedCentralPubMedGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  • Mariko Tsukune
    • 1
  • Yo Kobayashi
    • 2
  • Tomoyuki Miyashita
    • 3
  • G. Masakatsu Fujie
    • 3
  1. 1.Graduate School of Creative Science/Institute of Advanced Active Aging ResearchWaseda UniversityTokyoJapan
  2. 2.Faculty of Science and Engineering/Research Institute of Science and EngineeringWaseda UniversityTokyoJapan
  3. 3.Faculty of Science and EngineeringWaseda UniversityTokyoJapan

Personalised recommendations