Fiducial-based registration with a touchable region model



Image-guided surgery requires registration between an image coordinate system and an intraoperative coordinate system that is typically referenced to a tracking device. In fiducial-based registration methods, this is achieved by localizing points (fiducials) in each coordinate system. Often, both localizations are performed manually, first by picking a fiducial point in the image and then by using a hand-held tracked pointer to physically touch the corresponding fiducial on the patient. These manual procedures introduce localization error that is user-dependent and can significantly decrease registration accuracy. Thus, there is a need for a registration method that is tolerant of imprecise fiducial localization in the preoperative and intraoperative phases.


We propose the iterative closest touchable point (ICTP) registration framework, which uses model-based localization and a touchable region model. This method consists of three stages: (1) fiducial marker localization in image space, using a fiducial marker model, (2) initial registration with paired-point registration, and (3) fine registration based on the iterative closest point method.


We perform phantom experiments with a fiducial marker design that is commonly used in neurosurgery. The results demonstrate that ICTP can provide accuracy improvements compared to the standard paired-point registration method that is widely used for surgical navigation and surgical robot systems, especially in cases where the surgeon introduces large localization errors.


The results demonstrate that the proposed method can reduce the effect of the surgeon’s localization performance on the accuracy of registration, thereby producing more consistent and less user-dependent registration outcomes.

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  1. 1.

    Arun K, Huang T, Blostein S (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5):698–700

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  3. 3.

    Billings S, Kang HJ, Cheng A, Boctor E, Kazanzides P, Taylor R (2015) Minimally invasive registration for computer-assisted orthopedic surgery: combining tracked ultrasound and bone surface points via the P-IMLOP algorithm. Int J CARS 10(6):761–771

    Article  Google Scholar 

  4. 4.

    Fattori G, Riboldi M, Desplanques M, Tagaste B, Pella A, Orecchia R, Baroni G (2012) Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications. IEEE Trans Biomed Eng 59(8):2191–2199

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Grimson W, Kikinis R, Jolesz F, Black P (1999) Image-guided surgery. Sci Am 280(6):54–61

    Article  Google Scholar 

  6. 6.

    Gu L, Peters T (2004) 3D automatic fiducial marker localization approach for frameless stereotactic neuro-surgery navigation. Med Imaging Augment Real 3150:329–336

    Article  Google Scholar 

  7. 7.

    Ma B, Ellis R (2003) Robust registration for computer-integrated orthopedic surgery: laboratory validation and clinical experience. Med Image Anal 7(3):237–250

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Maurer CR Jr, Fitzpatrick JM, Wang MY, Galloway RL Jr, Maciunas RJ, Allen GS (1997) Registration of head volume images using implantable fiducial markers. IEEE Trans Med Imaging 16(4):447–462

  9. 9.

    Moghari MH, Abolmaesumi P (2007) Point-based rigid-body registration using an unscented kalman filter. IEEE Trans Med Imaging 26(12):1708–1728

    Article  PubMed  Google Scholar 

  10. 10.

    Nagy DA, Haidegger T, Yaniv Z (2014) A framework for semi-automatic fiducial localization in volumetric images. Augment Environ Comput Assist Interv 8678:138–148

    Google Scholar 

  11. 11.

    Perry J, Rosenbaum A, Lunsford D, Swink C, Zorub D (1980) Computed tomography-guided stereotactic surgery: conception and development of a new stereotactic methodology. Neurosurgery 7(4):376–381

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Pieper SD, Halle M, Kikinis R (2004) 3D Slicer. In: IEEE International Symposium on Biomedical Imaging (ISBI), 15–18 April, Arlington, VA, USA. pp 632–635

  13. 13.

    Wang M, Song Z (2008) Automatic detection of fiducial marker center based on shape index and curvedness. Med Imaging Augment Real 5128:81–88

    Article  Google Scholar 

  14. 14.

    Wang M, Song Z (2009) Automatic localization of the center of fiducial markers in 3D CT/MRI images for image-guided neurosurgery. Pattern Recognit Lett 30:414–420

    CAS  Article  Google Scholar 

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This work was supported by NSF NRI 1208540.

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Correspondence to Sungmin Kim.

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The authors declare that they have no conflict of interest.

Ethical approval

A human subject study was performed, with institutional review board approval (HIRB00003967), to evaluate the proposed registration method. No animal studies were performed.

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This article does not contain patient data.

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Kim, S., Kazanzides, P. Fiducial-based registration with a touchable region model. Int J CARS 12, 277–289 (2017).

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  • Registration
  • Fiducial marker
  • Surgical navigation
  • Surgical robot