Tactile sensor-based real-time clustering for tissue differentiation

  • Ralf Stroop
  • Makoto Nakamura
  • Johan Schoukens
  • David Oliva UribeEmail author
Original Article



Reliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon’s visual aspect and tactile impression of the considered tissue, which is—due to inherent low brain consistency contrast—a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when—as for neuroendoscopy—tactile perception will be damped or—as for surgical robotic applications—will not be a priori existent. Here, we present the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.


A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E1 and E2 for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate cluster algorithm.


Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.


Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising.


Tactile sensor Tissue differentiation Bimorph Multisine excitation Brain tumor resection 



This work was founded by the European Research Council (ERC) Advanced ERC Grant No. 320378–SNLSID (Johan Schoukens). David Oliva Uribe would like to thank the support and motivation of Beatriz Vizcaino. In addition, the authors thank to the personnel of the Slaughter House Anderlecht for kindly providing the ex vivo samples.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants performed by any of the authors.

Ethical approval

For this type of study, formal consent is not required. This article does not contain patient data.


  1. 1.
    Kuhnt D, Bauer MHA, Nimsky C (2012) Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges. Crit Rev Biomed Eng 40(3):175–185CrossRefGoogle Scholar
  2. 2.
    van Leyen K, Klotzsch C, Harrer JU (2011) Brain tumor imaging with transcranial sonography: state of the art and review of the literature. Ultraschall in der Medizin 32(6):572–581CrossRefGoogle Scholar
  3. 3.
    Finke M, Kantelhardt S, Schlaefer A, Bruder R, Lankenau E, Giese A, Schweikard A (2012) Automatic scanning of large tissue areas in neurosurgery using optical coherence tomography. Int J Med Robot Comput Assist Surg MRCAS 8(3):327–336CrossRefGoogle Scholar
  4. 4.
    Kirsch M, Schackert G, Salzer R, Krafft C (2010) Raman spectroscopic imaging for in vivo detection of cerebral brain metastases. Anal Bioanal Chem 398(4):1707–1713CrossRefGoogle Scholar
  5. 5.
    Steiner G, Sobottka SB, Koch E, Schackert G, Kirsch M (2011) Intraoperative imaging of cortical cerebral perfusion by time-resolved thermography and multivariate data analysis. J Biomed Opt 16(1):16001CrossRefGoogle Scholar
  6. 6.
    Colditz MJ, van Leyen K, Jeffree RL (2012) Aminolevulinic acid (ALA)-protoporphyrin IX fluorescence guided tumour resection. Part 2: theoretical, biochemical and practical aspects. J Clin Neurosci 19(12):1611–1616CrossRefGoogle Scholar
  7. 7.
    Kern TA (2009) Engineering haptic devices: a beginner’s guide for engineers. Springer, DordrechtCrossRefGoogle Scholar
  8. 8.
    Lee MH, Nicholls HR (1999) Review article tactile sensing for mechatronics—a state of the art survey. Mechatronics 9(1):1–31CrossRefGoogle Scholar
  9. 9.
    Eltaib MEH, Hewit JR (2003) Tactile sensing technology for minimal access surgery—a review. Mechatronics 13(10):1163–1177CrossRefGoogle Scholar
  10. 10.
    Rahim RA, Waduth MFA, Jaafar HI, Ayob NMN, Leow PL (2012) Current trend of tactile sensor in advanced applications. Sens Transducers 143(8):32–43Google Scholar
  11. 11.
    Tiwana MI, Redmond SJ, Lovell NH (2012) A review of tactile sensing technologies with applications in biomedical engineering. Sens Actuators A 179:17–31CrossRefGoogle Scholar
  12. 12.
    Lucarotti C, Oddo CM, Vitiello N, Carrozza MC (2013) Synthetic and bio-artificial tactile sensing: a review. Sensors 13(2):1435–1466CrossRefGoogle Scholar
  13. 13.
    Saccomandi P, Schena E, Oddo C, Zollo L, Silvestri S, Guglielmelli E (2014) Microfabricated tactile sensors for biomedical applications: a review. Biosensors 4(4):422–448CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Bonomo C, Brunetto P, Fortuna L, Giannone P, Graziani S, Strazzeri S (2008) A tactile sensor for biomedical applications based on IPMCs. IEEE Sens J 8(8):1486–1493CrossRefGoogle Scholar
  15. 15.
    Brunetto P, Fortuna L, Giannone P, Graziani S, Pagano F (2010) A resonant vibrating tactile probe for biomedical applications based on IPMC. IEEE Trans Instrum Meas 59(5):1453–1462CrossRefGoogle Scholar
  16. 16.
    Hemsel T, Stroop R, Uribe DO, Wallaschek J (2007) Resonant vibrating sensors for tactile tissue differentiation. J Sound Vib 308(3–5):441–446CrossRefGoogle Scholar
  17. 17.
    Tanaka Y, Yu Q, Doumoto K, Sano A, Hayashi Y, Fujii M, Kajita Y, Mizuno M, Wakabayashi T, Fujimoto H (2010) Development of a real-time tactile sensing system for brain tumor diagnosis. Int J Comput Assist Radiol Surg 5(4):359–367CrossRefGoogle Scholar
  18. 18.
    Johannsmann D, Langhoff A, Bode B, Mpoukouvalas K, Heimann A, Kempski O, Charalampaki P (2013) Towards in vivo differentiation of brain tumor versus normal tissue by means of torsional resonators. Sens Actuators A 190:25–31CrossRefGoogle Scholar
  19. 19.
    Uribe DO, Schoukens J, Stroop R (2018) Improved tactile resonance sensor for robotic assisted surgery. Mech Syst Signal Process 99:600–610CrossRefGoogle Scholar
  20. 20.
    Uribe DO, Stroop R, Wallaschek J (2009) Piezoelectric self-sensing system for tactile intraoperative brain tumor delineation in neurosurgery. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society, vol 2009. IEEE Engineering in Medicine and Biology Society, pp 737–740Google Scholar
  21. 21.
    Uribe DO, Zhu H, Wallaschek J (2010) Automated measurement system for mechanical characterization of soft tissues and phantoms. In: 2010 International conference on electronic devices, systems and applications (ICEDSA), pp 227–231Google Scholar
  22. 22.
    Madsen EL, Hobson MA, Shi H, Varghese T, Frank GR (2005) Tissue-mimicking agar/gelatin materials for use in heterogeneous elastography phantoms. Phys Med Biol 50(23):5597–5618CrossRefPubMedCentralGoogle Scholar
  23. 23.
    Brunetto P, Fortuna L, Giannone P, Graziani S, Strazzeri S (2010) Static and dynamic characterization of the temperature and humidity influence on IPMC actuators. IEEE Trans Instrum Meas 59(4):893–908CrossRefGoogle Scholar
  24. 24.
    Kollar I (1993) On frequency-domain identification of linear systems. IEEE Trans Instrum Meas 42(1):2–6CrossRefGoogle Scholar
  25. 25.
    Cheng L, Xia X, Scriven LE, Gerberich WW (2005) Spherical-tip indentation of viscoelastic material. Mech Mater 37(1):213–226CrossRefGoogle Scholar
  26. 26.
    Seber GAF (2004) Multivariate observations. Wiley, New YorkGoogle Scholar
  27. 27.
    Green MA, Bilston LE, Sinkus R (2008) In vivo brain viscoelastic properties measured by magnetic resonance elastography. NMR Biomed 21(7):755–764CrossRefGoogle Scholar
  28. 28.
    Jalkanen V, Andersson BM, Bergh A, Ljungberg B, Lindahl OA (2006) Resonance sensor measurements of stiffness variations in prostate tissue in vitro—a weighted tissue proportion model. Physiol Meas 27(12):1373–1386CrossRefGoogle Scholar
  29. 29.
    Sasaki T, Haruta M, Omata S (2014) Ct elastography: a pilot study via a new endoscopic tactile sensor. Open J Biophys. Google Scholar
  30. 30.
    Omata S, Terunuma Y (1992) New tactile sensor like the human hand and its applications. Sens Actuators A. Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of NeurosurgeryAcademic Hospital Cologne-MerheimCologneGermany
  2. 2.Department of Engineering Technology (INDI)Vrije Universiteit BrusselBrusselsBelgium

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