Automatic bone detection and soft tissue aware ultrasound–CT registration for computer-aided orthopedic surgery

  • Wolfgang WeinEmail author
  • Athanasios Karamalis
  • Adrian Baumgartner
  • Nassir Navab
Original Article



The transfer of preoperative CT data into the tracking system coordinates within an operating room is of high interest for computer-aided orthopedic surgery. In this work, we introduce a solution for intra-operative ultrasound–CT registration of bones.


We have developed methods for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a novel bone-specific feature descriptor and was thoroughly evaluated on both in-vivo and ex-vivo data. A global optimization strategy aligns the bone surface, followed by a soft tissue aware intensity-based registration to provide higher local registration accuracy.


We evaluated the system on femur, tibia and fibula anatomy in a cadaver study with human legs, where magnetically tracked bone markers were implanted to yield ground truth information. An overall median system error of \(3.7\) mm was achieved on 11 datasets.


Global and fully automatic registration of bones aquired with ultrasound to CT is feasible, with bone detection and tracking operating in real time for immediate feedback to the surgeon.


Registration Ultrasound Navigation CAOS 


Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    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 Imag 25:312–323CrossRefGoogle Scholar
  2. 2.
    Beitzel J, Ahmadi SA, Karamalis A, Wein W, Navab N (2012) Ultrasound bone detection using patient-specific CT prior. In: 34. International conference of the IEEE engineering in medicine and biology society (EMBC), pp 2664–2667Google Scholar
  3. 3.
    Brounstein A, Hacihaliloglu I, Guy P, Hodgson A, Abugharbieh R (2011) Towards real-time 3D US to CT bone image registration using phase and curvature feature based GMM matching. In: Medical image computing and computer-assisted intervention, MICCAI, pp 235–242Google Scholar
  4. 4.
    Gill S, Abolmaesumi P, Fichtinger G, Boisvert J, Pichora D, Borshneck D, Mousavi P (2012) Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine. Med Image Anal 16(3):662–674CrossRefPubMedGoogle Scholar
  5. 5.
    Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783CrossRefPubMedGoogle Scholar
  6. 6.
    Hacihaliloglu I, Abugharbieh R, Hodgson A, Rohling R (2008) Bone segmentation and fracture detection in ultrasound using 3D local phase features. In: Medical image computing and computer-assisted intervention, MICCAI, pp 287–295Google Scholar
  7. 7.
    Hacihaliloglu I, Brounstein A, Guy P, Hodgson A, Abugharbieh R (2012) 3D ultrasound–CT registration in orthopaedic trauma using GMM registration with optimized particle simulation-based data reduction. In: Medical image computing and computer-assisted intervention, MICCAI, pp 82–89Google Scholar
  8. 8.
    Heinrich MP, Jenkinson M, Bhushan M, Matin T, Gleeson FV, Brady SM, Schnabel JA (2012) Mind: modality independent neighbourhood descriptor for multi-modal deformable registration. Med Image Anal 16(7):1423–1435CrossRefPubMedGoogle Scholar
  9. 9.
    Jain AK, Taylor RH (2004) Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a bayesian probabilistic framework. In: Proceedings of SPIE medical imaging: ultrasonic imaging and signal processingGoogle Scholar
  10. 10.
    Jesorsky O, Kirchberg K, Frischholz R (2001) Robust face detection using the hausdorff distance. In: Proceedings of third international conference on audio- and video-based biometric person authentication, pp 90–95Google Scholar
  11. 11.
    Kaelo P, Ali MM (2006) Some variants of the controlled random search algorithm for global optimization. J Optim Theory Appl 130:253–264CrossRefGoogle Scholar
  12. 12.
    Karamalis A, Wein W, Klein T, Navab N (2012) Ultrasound confidence maps using random walks. Med Image Anal 16(6):1101–1112CrossRefPubMedGoogle Scholar
  13. 13.
    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 (MRCAS) 3(4):341–348CrossRefGoogle Scholar
  14. 14.
    Penney G, Barratt D, Chan C, Slomczykowski M, Carter T, Edwards P, Hawkes D (2006) Cadaver validation of intensity-based ultrasound to CT registration. Med Image Anal 10:385395CrossRefGoogle Scholar
  15. 15.
    Penney G, Blackall J, Hamady M, Sabharwal T, Adam A, Hawkes D (2004) Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal 8(1):81–91CrossRefPubMedGoogle Scholar
  16. 16.
    Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans Med Imag 20(10):1038–1049CrossRefGoogle Scholar
  17. 17.
    Wachinger C, Navab N (2012) Entropy and Laplacian images: structural representations for multi-modal registration. Med Image Anal 16(1):1–17CrossRefPubMedGoogle Scholar
  18. 18.
    Wein W, Brunke S, Khamene A, Callstrom M, Navab N (2008) Automatic CT–ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12:577–585CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Wolfgang Wein
    • 1
    Email author
  • Athanasios Karamalis
    • 2
  • Adrian Baumgartner
    • 3
  • Nassir Navab
    • 2
  1. 1.ImFusion GmbHMunichGermany
  2. 2.Computer Aided Medical Procedures (CAMP)TU MunichMunichGermany
  3. 3.Synthes GmbHLangendorfSwitzerland

Personalised recommendations