Abstract
Automatic identification and extraction of bone contours from x-ray images is the first essential task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The initialization is solved by an Estimation of Bayesian Network Algorithm to fit a multiple component geometrical model to the x-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Our experimental results demonstrate its performance and efficacy even when part of the images are occluded.
This is a preview of subscription content, log in via an institution to check access.
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
Chen, Y., et al.: Automatic Extraction of Femur Contours from Hip X-ray Images. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 200–209. Springer, Heidelberg (2005)
de Luis-Garcia, R., et al.: A Fully Automatic Algorithm for Contour Detection of Bones in Hand Radiographs Using Active Contours. In: ICIP 2003, Part. III, pp. 421–424 (2003)
Roberts, M.G., et al.: Automatic segmentation of lumbar vertebrae on digitised radiographs using linked active appearance models. In: MIUA 2006, Part. I, pp. 120–124 (2006)
Gottschling, H., et al.: Intraoperative, Fluoroscopy-based Planning for Complex Osteotomies of the Proximal Femur. The International Journal of Medical Robotics & Computer Assisted Srugery 1(3), 67–73 (2005)
Bartolini, F., et al.: Model-based Extraction of Femoral Medulla Ducts from Radiographic Images. Image and Vision Computing 22, 173–182 (2004)
Tian, T.P., et al.: Computing Neck-Shaft Angle of Femur for X-ray Fracture Detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 82–89. Springer, Heidelberg (2003)
Benameur, S., et al.: 3D/2D Registration and Segmentation of Scoliotic Vertebrae Using Statistical Models. Computerized Medical Imaging and Graphics 27(5), 321–337 (2003)
Zheng, G., Nolte, L.-P.: Surface Reconstruction of Bone from X-ray Images and Point Distribution Model Incorporating a Novel Method for 2D-3D Correspondence. In: CVPR 2006, Part II, pp. 2237–2244 (2006)
Lamecker, H., et al.: Atlas-based 3D-Shape Reconstruction from X-ray Images. In: ICPR 2006, Part I, pp. 371–374 (2006)
Tang, T.S., Ellis, R.E.: 2D/3D Deformable Registration Using a Hybrid Atlas. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005, Part II. LNCS, vol. 3750, pp. 223–230. Springer, Heidelberg (2005)
Behiels, G., et al.: Active Shape Model-based Segmentation of Digital X-ray Images. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 128–137. Springer, Heidelberg (1999)
Howe, B., et al.: Hierarchical Segmentation of Cervical and Lumbar Vertebrae Using a Customised Generalized Hough Transform and Extensions to Active Appearance Models. In: Proc. IEEE 6th SSIAI, March 2004, pp. 182–186 (2004)
Langs, G., et al.: Determining Position and Fine Shape Detail in Radiological Anatomy. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 532–539. Springer, Heidelberg (2003)
Seise, M., et al.: Probabilistic Segmentation of the Knee Joint from X-ray Images. In: MIUA 2006, pp. 110–114 (2006)
de Bruijne, M., Nielsen, M.: Image Segmentation by Shape Particle Filtering. In: ICPR 2004, Part. III (2004)
Zheng, G., et al.: 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, H.-Y. (eds.) ACCV 2006, Part II. LNCS, vol. 3852, pp. 52–60. Springer, Heidelberg (2006)
Ma, B., Ellis, R.E.: Surface-based Registration with a Particle Filter. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004, Part I. LNCS, vol. 3216, pp. 566–573. Springer, Heidelberg (2004)
Lee, M.W., Cohen, I.: Human Upper Body Pose Estimation in Static Images. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004, Part II. LNCS, vol. 3022, pp. 126–138. Springer, Heidelberg (2004)
Sigal, L., et al.: Tracking loose-limbed people. In: CVPR 2004, Part. 1, pp. 421–428 (2004)
Wu, Y., et al.: Tracking articulated body by dynamic Markov network. In: ICCV 2003, vol. 1, pp. 1094–1101 (2003)
Coughlan, J., Ferreira, S.: Finding deformable shapes using loopy belief propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 453–468. Springer, Heidelberg (2002)
Rangarajan, A., et al.: A Bayesian Network Framework for Relational Shape Matching. In: ICCV 2003, pp. 671–678 (2003)
Dong, X., Zheng, G.: Fully Automatic Determination of Morphological Parameters of Proximal Femur from Calibrated Fluoroscopic Images Through Particle Filtering. In: Campilho, A., Kamel, M. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 535–546. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, X., Zheng, G. (2008). Automatic Extraction of Proximal Femur Contours from Calibrated X-Ray Images Using 3D Statistical Models. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-79982-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79981-8
Online ISBN: 978-3-540-79982-5
eBook Packages: Computer ScienceComputer Science (R0)