Segmenting lumbar vertebrae in digital video fluoroscopic images through edge enhancement

2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV)

Volumn 1, Issue , 2004, Pages 665-670

  Wong, Shu Fai ^a   Wong, Kwan Yee Kenneth ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EDGE ENHANCEMENTS; LUMBER VERTEBRAE; SUPPORT VECTOR MACHINES; VIDEO FLUOROSCOPY;

ALGORITHMS; BONE; EDGE DETECTION; IMAGE ANALYSIS; MEDICAL IMAGING; PATIENT TREATMENT; PATTERN RECOGNITION; TEXTURES;

IMAGE SEGMENTATION;

EID: 21244495602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. A. C. Breen, R. Allen, and A. Morris, "A digital video-fluoroscopic technique for spine kinematics," Journal of Medical Engineering and Technology, vol. 13, pp. 109-113, 1989.
2. C. Simonis, R. Allen, and R. Cloke, "Spatial analysis of the movement of the spine: Application of parallel computing in the field of spine biomechanics," Transputer Applications, pp. 35-38, 1993.
3. J. M. Muggleton and R. Allen, "Automatic location of vertebrae in digitized videofluoroscopic images of the lumbar spine," Medical Engineering and Physics, vol. 19, pp. 77-89, 1997.
4. C. Cardan and R. Allen, "Measurement of spine motion for diagnosis of mechanical problems," Journal of Simulation Modeling in Medicine, vol. 1, no. 1, pp. 15-19, 2000.
5. W. H. Page, W. Monteith, and L. Whitehead, "Dynamic spinal analysis - fact and fiction," Chiropractic journal of Australia, vol. 23, no. 3, pp. 82-85, 1993.
6. P. P. Smyth, C. J. Taylor, and J. E. Adams, "Automatic measurement of vertebral shape using active shape models," Image and Vision Computing, vol. 15, pp. 575-581, 1997.
7. Y. Zheng, M. S. Nixon, and R. Allen, "Automated segmentation of lumbar vertebrae in digital videofluoroscopic images," IEEE Trans. Medicial Imaging, vol. 23, no. 1, pp. 45-52, 2004.
8. J. Woods, "Two dimensional discrete markov random fields," IT, vol. 18, pp. 232-240, 1972.
9. J.Woods, "Markov image modeling," AC, vol. 23, pp. 846-850, October 1978.
10. R. Chellappa and A. Jain, Markov Random Fields: Theory and Applications. Academic Press, 1993.
11. S. Krishnamachari and R. Chellappa, "Multiresolution gauss-markov random-field models for texture segmentation," IP, vol. 6, pp. 251-267, February 1997.
12. R. Dubes and A. Jain, "Random field models in image analysis," AppStat, vol. 16, no. 2, pp. 131-164, 1989.
13. G. Cross and A. Jain, "Markov random field texture models," PAMI, vol. 5, pp. 25-39, January 1983.
14. H. Ai, L. Liang, and G. Xu, "Face detection based on template matching and support vector machines," in ICIP01, pp. I:1006-1009, 2001.
15. R. Fransens, J. DePrins, and L. J. V. Gool, "Svm-based nonparametric discriminant analysis, an application to face detection," in ICCV03, pp. 1289-1296, 2003.
16. B. Scholkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods - Support Vector Learning. Cambridge, MA: MIT Press, 1999.
17. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," in Proc. Int. Conf. on Computer Vision, pp. 259-268, 1987.
18. D. J. Williams and M. Shah, "A fast algorithm for active contours," in Proc. Int. Conf. on Computer Vision, pp. 592-595, 1990.
19. A. Blake and M. Isard, Active Contours. Springer, 1998.
20. M. Isard and A. Blake, "Contour tracking by stochastic propagation of conditional density," in European Conference on Computer Vision, pp. 343-356, 1996.