Felly automatic determination of morphological parameters of proximal femur from calibrated fluoroscopic images through particle filtering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4142 LNCS, Issue , 2006, Pages 535-546

  Dong, Xiao ^a   Zheng, Guoyan ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; COMPUTATION THEORY; MEDICAL IMAGING; PATIENT TREATMENT;

AUTOMATIC DETERMINATION; FLUOROSCOPIC IMAGES; PROXIMAL FEMUR;

BONE;

EID: 33749664569     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11867661_48     Document Type: Conference Paper

References (14)

1. D.B. Burr D.B., Cook L.T., Martin N.L., and Asher M.: Measurement accuracy of proximal femoral geometry using biplanar radiography. J Pediatr Orthop (1981), 1(2): 171-9
2. Le Bras A., Laporte S., Bousson V., et al.: 3D reconstruction of the proximal femur with low-dose digital stereoradiography. CompAid Surg (2004), 9(3): 51-57
3. Ron O., Joskowicz L., Milgrom C., and Simkin A.: Computer-based periaxial rotation measurement for aligning fractured femur fragments from CT: a feasibility study. Comp Aid Surg (2002), 7: 332-341.
4. Burkhardt S., Roth M., Burgkart R., and Schweikard A.: A new system for completely MR-based computer assisted orthopaedic surgery. International Congress Series(2003), Vol. 1256, page 1342, Elsevier, Amsterdam
5. Gottschling H., Roth M., Schweikard A., and Burgkart R.: Intraoperative, fluoroscopy-based planning for complex osteotomies of the proximal femur. The International Journal of Medical Robotics & Computer Assisted Surgery(2005), 1(3): 67-73
6. Hofstetter R., Slomczykowski M., Sati M., Nolte L.-P.: Fluoroscopy as an image means for computer-assisted surgical navigation. Comp Aid Surg (1999), 4(2): 65-76
7. Isard M. and Blake A.: Contour tracking by stochastic propagation of conditional density. ECCV 1996, pp. 343-356, Cambridge, UK
8. Lee M. W. and Cohen I.: Human upper body pose estimation in static images. ECCV 2004, LNCS 3022, pp. 126-138
9. Wu Y., Hua G. and Yu T.: Tracking articulated body by dynamic Markov network. ICCV 2003, pp. 1094 - 1101, Nice, France
10. Sudderth E.B., Mandel M.I., Freeman W.T., and Willsky A.S.: Visual hand tracing using nonparametric belief propagation. IEEE CVPR workshop on Generative Model based Vision (2004).
11. Kanazawa K., Killer D., and Russell S.: Stochastic simulation algorithms for dynamic probabilistic networks. Proc. Uncertainty in Artificial Intelligence (1995): 346 - 351
12. Mahaisavariya B., Sitthiseripratip K., Tongdee T., et al.: Morphological study of the proximal femur: a new method of geometrical assessment using 3-dimensional reverse engineering. Med Eng Phys(2002), 24: 617 - 622
13. Cootes T. F. and Taylor C. J.: Statistical models of appearance for computer vision. Technical Report, Imaging Science and Biomedical Engineering, University of Manchester, UK, March 2004.
14. Andrieu C. and Doucet A.: Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE T. Image Process (1999), 47(10): 456-463.