Biomedical Engineering Reference
In-Depth Information
65. Lachaud, J. O., & Montanvert, A. (1999). Deformable meshes with automated topology
changes for coarse-to-fine three-dimensional surface extraction.
Medical Image Analysis
,
3
(2), 187-207.
66. Pons, J. P., & Boissonnat, J. D. (2007). Delaunay deformable models: Topology-adaptive
meshes based on the restricted delaunay triangulation. In
2007 IEEE Conference on Computer
Vision and Pattern Recognition
(vol. 13, pp. 384-394). IEEE.
67. Gilles, B. (2007). Anatomical and kinematical modelling of the musculoskeletal system from
MRI. Phd Thesis, University of Geneva, Aug 2007.
68. Szeliski, R., & Tonnesen, D. (1992). Surface modeling with oriented particle systems.
ACM
SIGGRAPH Computer Graphics
,
26
(2), 185-194.
69. Lombardo, J. C. (2004).
Modélisation dobjets déformables avec un système de particules
orientées
. Phd, Université Joseph Fourier, Grenoble.
70. Cootes, T. F., Hill, A., Taylor, C. J., & Haslam, J. (1994). The use of active shape models for
locating structures in medical images.
Image and Vision Computing
,
12
(6), 355-366.
71. Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models—Their
training and application.
Computer Vision and Image Understanding
,
61
(1), 38-59.
72. Yushkevich, P. A., Zhang, H., & Gee, J. C. (2005). Statistical modeling of shape and appear-
ance using the continuous medial representation.
Medical Image Computing and Computer-
Assisted Intervention
,
8
(Pt 2), 725-732.
73. Gower, J. C. (1975). Generalized procrustes analysis.
Psychometrika
,
40
(1), 33-51.
74. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space.
Philo-
sophical Magazine
,
2
(6), 559-572.
75. Everson, R., & Roberts, S. (2000). Inferring the eigenvalues of covariance matrices from
limited, noisy data.
IEEE Transactions on Signal Processing
,
48
(7), 2083-2091.
76. Cootes, T. F., & Taylor, C. J. (1995). Combining point distribution models with shape models
based on finite element analysis.
Image and Vision Computing
,
13
(5), 403-409.
77. Tölli, T., Koikkalainen, J., Lauerma, K., & Lötjönen, J. (2006). Artificially enlarged training
set in image segmentation.
Medical Image Computing and Computer-Assisted Intervention
,
9
(Pt 1), 75-82.
78. Ginneken, B. V., Frangi, A. F., Staal, J. J., Bart, M., Romeny, H., Viergever, M. A., van
Ginneken, B., & ter Haar Romeny, B. M. (2002). Active shape model segmentation with
optimal features.
IEEE Transactions on Medical Imaging
,
21
(8), 924-933.
79. Lamecker, H., Kamer, L., Wittmers, A., Zachow, S., Schramm, A., Noser, H., & Hammer,
B. (2007). A method for the three-dimensional statistical shape analysis of the bony orbit.
Proceedings of Computer Aided Surgery around the Head (CAS-H)
, pp. 2-5.
80. Cootes, T., Edwards, G., & Taylor, C. (1998). Active appearance models. In H. Burkhardt
& B. Neumann (Eds.),
Computer Vision ECCV98, Lecture Notes in Computer Science
(vol.
1407, pp. 484-498). Berlin/Heidelberg: Springer.
81. Olabarriaga, S. D., Breeuwer, M., & Niessen, W. J. (2004). Multi-scale statistical grey value
modelling for thrombus segmentation from CTA. In
Proceedings of MICCAI
(vol. 3216, pp.
467-474). Springer.
82. Shen, T., Li, H., &Huang, X. (2011). Active volume models for medical image segmentation.
IEEE Transactions on Medical Imaging
,
30
(3), 774-791.
83. Staib, L. H., &Duncan, J. S. (1992). Boundary findingwith parametrically deformablemodels.
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
14
(11), 1061-1075.
84. Székely, G., Kelemen, A., Brechbühler, C., & Gerig, G. (1996). Segmentation of 2-D and
3-D objects fromMRI volume data using constrained elastic deformations of flexible Fourier
contour and surface models.
Medical Image Analysis
,
1
(1), 19-34.
85. Horowitz, B., &Pentland, A. (1991). Recovery of non-rigidmotion and structure.
Proceedings
of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 1991,
pp. 325-330.
86. Bardinet, E., Cohen, L. D., & Ayache, N. (1998). A parametric deformable model to fit
unstructured 3D data.
Computer Vision and Image Understanding
,
71
(1), 39-54.