Uncertainty-driven non-parametric knowledge-based segmentation: The corpus callosum case

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

Volumn 3752 LNCS, Issue , 2005, Pages 198-209

  Taron, Maxime ^a   Paragios, Nikos ^a   Jolly, Marie Pierre ^b  

^a UNIVERSITÉ PARIS EST   (France)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BANDWIDTH; ESTIMATION; KNOWLEDGE BASED SYSTEMS; NONLINEAR SYSTEMS; POLYNOMIALS; VARIATIONAL TECHNIQUES;

IMPLICIT POLYNOMIALS; KNOWLEDGE BASED SEGMENTATION; UNCERTAINTIES; VARIABLE BANDWIDTH;

IMAGE SEGMENTATION;

EID: 33646582355     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11567646_17     Document Type: Conference Paper

References (19)

1. J. G. Bosch, S. C. Mitchell, B.P.F. Lelieveldt, F. Nikland, O. Kamp, M. Sonka, and J. H. Reiber. Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Trans. Medical Imaging, 21(11):1374-1383, 2002.
2. T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam. The use of active shape models for locating structures in medical images. Image and Vision Computing, 12(6):255-266, 1994.
3. T. F. Cootes and C. J. Taylor. Statistical models of appearance for computer vision. Technical Report, University of Manchester, 2004.
4. J. Duncan, A. Wang, A. Amini, R. Greene, L. Kier, J. Gore, J. Holahan, S. Shaywitz, J. Fletcher, R. Bronen, and B. Shaywitz. A MRI-based study of the corpus callosum in dyslexic and normal children. Neurology, 1996.
5. N. Duta and M. Sonka. Segmentation and interpretation of MR brain images: An improved active shape model. IEEE Trans. Medical Imaging, 17(6): 1049-1062, 1998.
6. M. Frumin, P. Golland, R. Kikinis, Y. Hirayasu, D. F. Salisbury, J. Hennen, C. C. Dickey, M. Anderson, F. A. Jolesz, W. E. L. Crimson, R. W. McCarley, and M. R. Shenton. Shape differences in the corpus callosum in first-episode schizophrenia and first-episode psychotic affective disorder. American Journal of Psychiatry, 159:866-868, 2002.
7. X. Huang, N. Paragios, and D. Metaxas. Registration of Structures in Arbitrary Dimensions: Implicit Representations, Mutual Information & Free-Form Deformations. Technical Report DCS-TR-0520, Division of Computer & Information Science, Rutgers University, 2003.
8. K. Kanatani. Uncertainty modeling and model selection for geometric inference. IEEE Trans. Pattern Anal. Mach. Intell., 26(10):1307-1319, 2004.
9. M. Leventon, E. Grimson, and O. Faugeras. Statistical Shape Influence in Geodesic Active Controus. In IEEE Conference on Computer Vision and Pattern Recognition, pages I:316-322, 2000.
10. A. Lundervold, N. Duta, T. Taxt, and A. Jain. Model-guided segmentation of corpus callosum in MR images. In CVPR, pages 1231-1238, 1999.
11. T. McInerney, G. Hamarneh, M. Shenton, and D. Terzopoulos. Deformable organisms for automatic medical image analysis. Medical Image Analysis, 6:251-266, 2002.
12. A. Mittal and N. Paragios. Motion-based background substraction using adaptive kernel density estimation. In Computer Vision and Pattern Recognition, volume 2, pages 302-309, 2004.
13. D. Mumford and J. Shah. Boundary detection by minimizing functionals. In IEEE Conference on Computer Vision and Pattern Recognition, pages 22-26, 1985.
14. S. Osher and J. Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on the Hamilton-Jacobi formulation. Journal of Computational Physics, 79:12-49, 1988.
15. N. Paragios, M. Rousson, and V. Ramesh. Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration. In European Conference on Computer Vision, pages 11:775-790, 2002.
16. D. Rueckert, L.I. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes. Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18:712-721, 1999.
17. T. Sederberg and S. Parry. Free-form deformation of solid geometric models. Proceedings SIGGRAPH '86, 20:151-160, 1986.
18. C. Stewart, C.-L. Tsai, and B. Roysam. The dual bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Trans. Med. Img., 22:1379-1394, 2003.
19. M. Wand and M. Jones. Kernel Smoothing. Chapman & Hall, 1995.