Abstract
Constructing anatomical shape from extremely sparse information is a challenging task. A priori information is often required to handle this otherwise ill-posed problem. In the present paper, we try to solve the problem in an accurate and robust way. At the heart of our approach lies the combination of a three-stage anatomical shape reconstruction technique and a dense surface point distribution model (DS-PDM). The DS-PDM is constructed from an already-aligned sparse training shape set using Loop subdivision. Its application facilitates the setup of point correspondences for all three stages of surface reconstruction due to its dense description. The proposed approach is especially useful for accurate and stable surface reconstruction from sparse information when only a small number of a priori training shapes are available. It adapts gradually to use more information derived from the a priori model when larger number of training data are available. The proposed approach has been successfully validated in a preliminary study on anatomical shape reconstruction of two femoral heads using only dozens of sparse points, yielding promising results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lavalle, S., Merloz, P., et al.: Echomorphing introducing an intra-operative imaging modality to reconstruct 3d bone surfaces for minimally invasive surgery. In: CAOS, pp. 38–39 (2004)
Hofstetter, R., Slomczykowski, M., et al.: Fluoroscopy as an imaging means for computer-assisted surgical navigation. Comp Aid Surg 4, 65–76 (2004)
Livyatan, H.M., Yaniv, Z., Joskowicz, J.: Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT. IEEE T Med Imaging 22, 1395–1406 (2004)
Evgeniou, T., Pontil, M., Poggio, T.: Regularization networks and support vector machines. Adv Comput Math 13, 1–50 (2000)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – their training and application. Comput Vis Image Und 61(1), 38–59 (1995)
Golland, P., Grimson, W.E.L., Shenton, M.E., Kikinis, R.: Small sample size learning for shape analysis of anatomical structures. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 72–82. Springer, Heidelberg (2000)
Blanz, V., Vetter, T.: Reconstructing the complete 3D shape of faces from partial information, pp. 295–302. it+ti Oldenburg Verlag (2002)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: SIGGRAPH 1999, pp. 187–194 (1999)
Lapeer, R.J.A., Prager, R.W.: 3D shape recovery of a newborn skull using thin-plate splines. Comput Med Imag Grap 24, 193–204 (2000)
Fleute, M., Lavallee, S.: Building a complete surface model from sparse data using statistical shape models: application to computer assisted knee surgery system. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 879–887. Springer, Heidelberg (1998)
Stindel, E., Briard, J.L., et al.: Bone morphing: 3D Morphological data for total knee arthroplasty. Comp Aid Surg 7, 156–168 (2002)
Chan, C.S., Edwards, P.J., Hawkes, D.J.: Integration of ultrasound-based registration with statistical shape models for computer-assisted orthopedic surgery. In: SPIE, Medical Imaging, pp. 414–424 (2003)
Rajamani, T.K., Nolte, L.-P., Styner, M.: Bone morphing with statistical models fro enhanced visualization. In: SPIE Medical Imaging, pp. 122–130 (2004)
Rajamani, T.K., Joshi, S., Styner, M.: Bone model morphing for enhanced surgical visualization. IEEE International Symposium on Biomedical Imaging, 1255–1258 (2004)
Rajamani, T.K., et al.: A novel and stable approach to anatomical structure morphing for enhanced intraoperative 3D visualization. SPIE Medical Imaging, 718–725 (2005)
Brechbuehler, C., Gerig, G., Kuebler, O.: Parameterization of closed surfaces for 3D shape description. Comput Vis Image Und
Davies, R.H., Twining, C.H., et al.: 3D statistical shape models using direct optimization of description length. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–20. Springer, Heidelberg (2002)
Loop, C.T.: Smooth subdivision surfaces based on triangles. M.S.Thesis, Department of Mathematics, University of Utah (August 1987)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE T Pattern Anal 14, 239–256 (1992)
Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. Image Vision Comput. 10, 145–155 (1992)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vision 13, 119–152 (1994)
Bookstein, F.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE T Pattern Anal 11, 567–585 (1989)
Aspert, N., Santa-Cruz, D., Ebrahimi, T.: MESH: Measuring errors between surfaces using the Hausdorff Distance. In: IEEE International Conference on Multimedia and Expo (ICME) 2002, pp. 705–708 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, G., Rajamani, K.T., Nolte, LP. (2006). Use of a Dense Surface Point Distribution Model in a Three-Stage Anatomical Shape Reconstruction from Sparse Information for Computer Assisted Orthopaedic Surgery: A Preliminary Study. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_6
Download citation
DOI: https://doi.org/10.1007/11612704_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
eBook Packages: Computer ScienceComputer Science (R0)