Minimally invasive registration for computer-assisted orthopedic surgery: combining tracked ultrasound and bone surface points via the P-IMLOP algorithm

  • Seth Billings
  • Hyun Jae Kang
  • Alexis Cheng
  • Emad Boctor
  • Peter Kazanzides
  • Russell Taylor
Original Article



We present a registration method for computer-assisted total hip replacement (THR) surgery, which we demonstrate to improve the state of the art by both reducing the invasiveness of current methods and increasing registration accuracy. A critical element of computer-guided procedures is the determination of the spatial correspondence between the patient and a computational model of patient anatomy. The current method for establishing this correspondence in robot-assisted THR is to register points intraoperatively sampled by a tracked pointer from the exposed proximal femur and, via auxiliary incisions, from the distal femur.


In this paper, we demonstrate a noninvasive technique for sampling points on the distal femur using tracked B-mode ultrasound imaging and present a new algorithm for registering these data called Projected Iterative Most-Likely Oriented Point (P-IMLOP). Points and normal orientations of the distal bone surface are segmented from ultrasound images and registered to the patient model along with points sampled from the exposed proximal femur via a tracked pointer.


The proposed approach is evaluated using a bone- and tissue-mimicking leg phantom constructed to enable accurate assessment of experimental registration accuracy with respect to a CT-image-based model of the phantom. These experiments demonstrate that localization of the femur shaft is greatly improved by tracked ultrasound. The experiments further demonstrate that, for ultrasound-based data, the P-IMLOP algorithm significantly improves registration accuracy compared to the standard ICP algorithm.


Registration via tracked ultrasound and the P-IMLOP algorithm has high potential to reduce the invasiveness and improve the registration accuracy of computer-assisted orthopedic procedures.


Oriented point registration Ultrasound-CT registration Computer-assisted orthopedic surgery  Total hip replacement surgery 



Support and funding for this work was provided by the National Science Foundation Graduate Research Fellowship Program, Johns Hopkins Internal Funds, and THINK Surgical, Inc.

Conflict of interest

Emad Boctor, Peter Kazanzides, and Russell Taylor have a grant from THINK Surgical, Inc. The authors declare that they have no other conflict of interest.


  1. 1.
    Garbini J, Kaiura R, Sidles J, Larson R, Matsen F (1987) Robotic instrumentation in total knee arthroplasty. In: 33rd Annual meeting, orthopaedic research societyGoogle Scholar
  2. 2.
    Paul HA, Bargar WL, Mittelstadt B, Musits B, Taylor RH, Kazanzides P, Zuhars J, Williamson B, Hanson W (1992) Development of a surgical robot for cementless total hip arthroplasty. Clin Orthop Relat Res 285:57–66PubMedGoogle Scholar
  3. 3.
    Taylor R, Mittelstadt B, Paul H, Hanson W, Kazanzides P, Zuhars J, Williamson B, Musits B, Glassman E, Bargar W (1994) An image-directed robotic system for precise orthopaedic surgery. IEEE Trans Robot Autom 10:261–275CrossRefGoogle Scholar
  4. 4.
    Kazanzides P, Zuhars J, Mittelstadt B, Taylor R (1992) Force sensing and control for a surgical robot. In: IEEE International conference on robotics and automation. pp 612–617Google Scholar
  5. 5.
    Nogler M, Maurer H, Wimmer C, Gegenhuber C, Bach C, Krismer M (2001) Knee pain caused by a fiducial marker in the medial femoral condyle: a clinical and anatomic study of 20 cases. Acta Orthop 72:477–480CrossRefGoogle Scholar
  6. 6.
    Cohan S (2001) ROBODOC achieves pinless registration. Int J Ind Robot 28:381–386CrossRefGoogle Scholar
  7. 7.
    Guéziec A, Kazanzides P, Williamson B, Taylor RH (1998) Anatomy-based registration of ct-scan and intraoperative x-ray images for guiding a surgical robot. IEEE Trans Med Imaging 17:715–728CrossRefPubMedGoogle Scholar
  8. 8.
    LaRose D, Bayouth J, Kanade T (2000) Transgraph: interactive intensity-based 2d/3d registration of x-ray and ct data. In: Hanson KM (ed) SPIE medical imaging 2000: image processing, vol 3979, pp 385–396Google Scholar
  9. 9.
    Otake Y, Armand M, Armiger RS, Kutzer MD, Basafa E, Kazanzides P, Taylor RH (2012) Intraoperative image-based multiview 2d/3d registration for image-guided orthopaedic surgery: incorporation of fiducial-based C-arm tracking and GPU-acceleration. IEEE Trans Med Imaging 31:948–962CrossRefPubMedGoogle Scholar
  10. 10.
    Sahay A, Witherspoon L, Bargar W (2004) Computer model-based study for minimally invasive THR femoral cavity preparation using the ROBODOC system. In: Computer Assisted Orthopaedic Surgery (CAOS). pp 314–316Google Scholar
  11. 11.
    Maurer CR, Gaston RP, Hill DLG, Gleeson MJ, Taylor MG, Fenlon MR, Edwards PJ, Hawkes DJ (1999) AcouStick: a tracked A-mode ultrasonography system for registration in image-guided surgery. In: Taylor C, Colchester A (eds) Medical image computing and computer-assisted intervention—MICCAI’99, pp 953–962Google Scholar
  12. 12.
    Amin DV, Kanade T, DiGioia AM, Jaramaz B (2003) Ultrasound registration of the bone surface for surgical navigation. Comput Aided Surg 8:1–16CrossRefPubMedGoogle Scholar
  13. 13.
    Chen TK, Abolmaesumi P, Pichora DR, Ellis RE (2005) A system for ultrasound-guided computer-assisted orthopaedic surgery. Comput Aided Surg 10:281–292CrossRefPubMedGoogle Scholar
  14. 14.
    Barratt D, Penney G, Chan C, Slomczykowski M, Carter T, Edwards P, Hawkes D (2006) Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery. IEEE Trans Med Imaging 25:312–323CrossRefPubMedGoogle Scholar
  15. 15.
    Foroughi P, Boctor E, Swartz M, Taylor R, Fichtinger G (2007) Ultrasound bone segmentation using dynamic programming. In: IEEE ultrasonics symposium. pp 2523–2526Google Scholar
  16. 16.
    Kowal J, Amstutz C, Langlotz F, Talib H, Ballester MG (2007) Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgery-an in vitro evaluation. Int J Med Robot Comput Assist Surg 3:341–348CrossRefGoogle Scholar
  17. 17.
    Hacihaliloglu I, Wilson DR, Gilbart M, Hunt MA, Abolmaesumi P (2013) Non-iterative partial view 3d ultrasound to ct registration in ultrasound-guided computer-assisted orthopedic surgery. Int J Comput Assist Radiol Surg 8:157–168CrossRefPubMedGoogle Scholar
  18. 18.
    Gonçalves P, Torres P, Santos F, António R, Catarino N, Martins J (2015) A vision system for robotic ultrasound guided orthopaedic surgery. J Intell Robot Syst 77:327–329Google Scholar
  19. 19.
    Jain AK, Taylor RH (2004) Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a bayesian probabilistic framework. In: Walker WF, Emelianov SY (eds) SPIE medical imaging 2004: ultrasonic imaging and signal processing, vol 5373, pp 131–142Google Scholar
  20. 20.
    Daanen V, Tonetti J, Troccaz J (2004) A fully automated method for the delineation of osseous interface in ultrasound images. In: Barillot C, Haynor DR, Hellier P (eds) Medical image computing and computer-assisted intervention—MICCAI 2004, pp 549–557Google Scholar
  21. 21.
    Billings S, Taylor R (2014) Iterative most likely oriented point registration. In: Golland P, Hata N, Barillot C, Hornegger J, Howe R (eds) Medical image computing and computer-assisted intervention—MICCAI 2014, pp 178–185Google Scholar
  22. 22.
    Besl P, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256CrossRefGoogle Scholar
  23. 23.
    Guo X, Cheng A, Zhang HK, Kang HJ, Etienne-Cummings R, Boctor EM (2014) Active echo: a new paradigm for ultrasound calibration. In: Golland P, Hata N, Barillot C, Hornegger J, Howe R (eds) Medical image computing and computer-assisted intervention—MICCAI 2014, pp 397–404Google Scholar
  24. 24.
    Cheng A, Guo X, Zhang HK, Kang HJ, Etienne-Cummings R, Boctor EM (2015) Active point out-of-plane ultrasound calibration. In: Yaniv ZR, Webster RJ (eds) SPIE medical imaging 2015: image-guided procedures, robotic interventions, and modeling, vol 9415. (in press)
  25. 25.
    Estépar RSJ, Brun A, Westin CF (2004) Robust generalized total least squares iterative closest point registration. In: Barillot C, Haynor DR, Hellier P (eds) Medical image computing and computer-assisted intervention—MICCAI 2004, pp 234–241Google Scholar
  26. 26.
    Segal A, Haehnel D, Thrun S (2009) Generalized-icp. In: Robotics: science and systems.
  27. 27.
    Maier-Hein L, Franz AM, dos Santos TR, Schmidt M, Fangerau M, Meinzer H, Fitzpatrick JM (2012) Convergent iterative closest-point algorithm to accommodate anisotropic and inhomogeneous localization error. IEEE Trans Pattern Anal Mach Intell 34:1520–1532CrossRefPubMedGoogle Scholar
  28. 28.
    Billings S, Boctor E, Taylor R (2015) Iterative most-likely point registration (imlp): a robust algorithm for computing optimal shape alignment. PLoS One 10:e0117688 doi: 10.1371/journal.pone.0117688
  29. 29.
    Moghari MH, Abolmaesumi P (2007) Point-based rigid-body registration using an unscented Kalman filter. IEEE Trans Med Imaging 26:1708–1728CrossRefPubMedGoogle Scholar
  30. 30.
    Pulli K (1999) Multiview registration for large data sets. In: Second international conference on 3-D digital imaging and modeling, pp 160–168Google Scholar
  31. 31.
    Lara C, Romero L, Caldern F (2008) A robust iterative closest point algorithm with augmented features. In: Gelbukh A, Morales EF (eds) MICAI 2008: advances in artificial intelligence, pp 605–614Google Scholar
  32. 32.
    Kang X, Armand M, Otake Y, Yau WP, Cheung P, Hu Y, Taylor R (2014) Robustness and accuracy of feature-based single image 2d–3d registration without correspondences for image-guided intervention. IEEE Trans Biomed Eng 61:149–161CrossRefGoogle Scholar
  33. 33.
    Baka N, Metz C, Schultz C, van Geuns RJ, Niessen W, van Walsum T (2014) Oriented gaussian mixture models for non-rigid 2d/3d coronary artery registration. IEEE Trans Med Imaging 33:1023–1034Google Scholar
  34. 34.
    Bushberg JT, Seibert JA, Leidholdt EM Jr, Boone JM (2011) The essential physics of medical imaging, 3rd edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  35. 35.
    Mardia K, Jupp P (2000) Directional statistics. Wiley, HobokenGoogle Scholar
  36. 36.
    Verma N, Kpotufe S, Dasgupta S (2009) Which spatial partition trees are adaptive to intrinsic dimension? In: UAI ’09 proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp 565–574Google Scholar
  37. 37.
    Larsson T (2008) An efficient ellipsoid-obb intersection test. J Graph GPU Game Tools 13:31–43CrossRefGoogle Scholar
  38. 38.
    King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758Google Scholar
  39. 39.
    Integrated Surgical Systems (1999) \(\text{ ROBODOC }^{\textregistered }\) User ManualGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Seth Billings
    • 1
  • Hyun Jae Kang
    • 1
  • Alexis Cheng
    • 1
  • Emad Boctor
    • 2
  • Peter Kazanzides
    • 1
  • Russell Taylor
    • 1
  1. 1.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA
  2. 2.Division of Medical Imaging Physics, Department of RadiologyJohns Hopkins Medical InstitutionsBaltimoreUSA

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