3D Automatic Fiducial Marker Localization Approach for Frameless Stereotactic Neuro-surgery Navigation

  • Lixu Gu
  • Terry Peters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3150)


Although frameless stereotactic neuro-surgical navigation systems are widely used in many neuro-surgical centers around the world, most of the systems still require the user to define the position of fiducial markers manually from patient scans, a procedure that is tedious, time consuming and often inaccurate. Some researchers have addressed this problem, but they acknowledge that their 2D image processing approach has limitation. We propose a new automatic approach for 3D localization of the fiducial markers, which provides higher 3D localization accuracy, and is independent of the geometry of the marker. Our approach includes three steps. First, sets of 3D morphological operations are employed to extract the candidate fiducial markers as the ”seeds”. Then a ”conditional dilation” technique is employed to reconstruct the regions of fiducials from the ”seeds” which are sifted by several knowledge-based rules. Lastly, the intensity-weighted centroid of each extracted fiducial region is calculated as our final fiducial position. The approach is validated by simulated datasets and a CT phantom scan where the average Fiducial Localization Error (FLE) is 0.37mm and 0.31mm, respectively.


Fiducial Marker High Intensity Level Stereotactic System Fiducial Localization Error Fiducial Region 
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  1. 1.
    Salles, D., Lufkin, R. (eds.): Minimally Invasive Therapy of the Brain. Thieme, New York, Stuttgart (1997)Google Scholar
  2. 2.
    Jolesz, F.A.: Image-guided Procedures and the Operating Room of the Future. Radiology 204, 601–612 (1997)Google Scholar
  3. 3.
    Finnis, K.W., Starreveld, Y.P., Parrent, A.G., Peters, T.M.: A 3-Dimensional Database of Deep Brain Functional Anatomy, and Its Application to Image-Guided Neurosurgery. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 1–8. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Maurer, C.R., Fitzpatrick, J.M.: A Review of Medical Image Registration. In: Maciunas, R.J. (ed.) Interactively active Image-Guided Neurosurgery, a book, pp. 17–44. American Association of Neurological Surgeons, Park Ridge (1993)Google Scholar
  5. 5.
    West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer, C.R., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography 21(4), 554–566 (1997)CrossRefGoogle Scholar
  6. 6.
    Maurer, C.R., Fitzpatrick, J.M., Wang, M.Y., Galloway, R.L., Maciunas, R.J., Allen, G.S.: Reg-istration of head volume images using implantable fiducial markers. IEEE Transactions on Medical Imaging 16, 447–462 (1997)CrossRefGoogle Scholar
  7. 7.
    Wang, M.Y., Maurer, C.R., Fitzpatrick, J.M., Maciunas, R.J.: An automatic Technique for Finding and Localizing Externally Attached Markers in CT and MR Volume Images of the Head. IEEE Transactions on Biomedical Engineering 43(6), 627–637 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lixu Gu
    • 1
    • 2
  • Terry Peters
    • 2
  1. 1.Computer ScienceShanghai Jiaotong UniversityShanghaiChina
  2. 2.Robarts Research InstituteLondonCanada

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