Abstract
Camera calibration is central to obtaining a quantitative image-to-physical-space mapping from stereo images acquired in the operating room (OR). A practical challenge for cameras mounted to the operating microscope is maintenance of image calibration as the surgeon’s field-of-view is repeatedly changed (in terms of zoom and focal settings) throughout a procedure. Here, we present an efficient method for sustaining a quantitative image-to-physical space relationship for arbitrary image acquisition settings (S) without the need for camera re-calibration. Essentially, we warp images acquired at S into the equivalent data acquired at a reference setting, S 0, using deformation fields obtained with optical flow by successively imaging a simple phantom. Closed-form expressions for the distortions were derived from which 3D surface reconstruction was performed based on the single calibration at S 0. The accuracy of the reconstructed surface was 1.05 mm and 0.59 mm along and perpendicular to the optical axis of the operating microscope on average, respectively, for six phantom image pairs, and was 1.26 mm and 0.71 mm for images acquired with a total of 47 arbitrary settings during three clinical cases. The technique is presented in the context of stereovision; however, it may also be applicable to other types of video image acquisitions (e.g., endoscope) because it does not rely on any a priori knowledge about the camera system itself, suggesting the method is likely of considerable significance.
Chapter PDF
References
Mirota, D.J., Ishii, M., Hager, G.D.: Vision-based navigation in image-guided interventions. Annual Review of Biomedical Engineering 13, 297–319 (2011)
Hemayed, E.E.: A survey of camera self-calibration. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 351–357 (2003)
Figl, M., Ede, C., Hummel, J., Wanschitz, F., Ewers, R., Bergmann, H., Birkfellner, W.: A fully automated calibration method for an optical see-through head-mounted operating microscope with variable zoom and focus. IEEE Trans. Med. Imag. 24(11), 1492–1499 (2005)
Willson, R.: Modeling and Calibration of Automated Zoom Lenses, CMU-RI-TR-94-03, Robotics Institute, Carnegie Mellon University (1994)
Edwards, P.J., King, A.P., Maurer, C.R., de Cunha, D.A., Hawkes, D.J., Hill, D.L., Gaston, R.P., Fenlon, M.R., Jusczyzck, A., Strong, A.J., Chandler, C.L., Gleeson, M.J.: Design and evaluation of a system for microscope-assisted guided interventions (MAGI). IEEE Trans. Med. Imag. 19(11), 1082–1093 (2000)
Ji, S., Fan, X., Roberts, D.W., Paulsen, K.D.: Cortical surface strain estimation using stereovision. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 412–419. Springer, Heidelberg (2011)
Liu, C.: Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. Massachusetts Institute of Technology (May 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ji, S., Fan, X., Roberts, D.W., Paulsen, K.D. (2014). Efficient Stereo Image Geometrical Reconstruction at Arbitrary Camera Settings from a Single Calibration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)