A. Agarwal and B. Triggs, Tracking Articulated Motion Using a Mixture of Autoregressive Models, European Conference on Computer Vision, pp.54-65, 2004.
DOI : 10.1007/978-3-540-24672-5_5

URL : https://hal.archives-ouvertes.fr/inria-00548550

A. Agarwal and B. Triggs, Tracking Articulated Motion Using a Mixture of Autoregressive Models, European Conference on Computer Vision, pp.54-65, 2004.
DOI : 10.1007/978-3-540-24672-5_5

URL : https://hal.archives-ouvertes.fr/inria-00548550

B. Appleton and C. Sun, Circular shortest paths by branch and bound, Pattern Recognition, vol.36, issue.11, pp.2513-2520, 2003.
DOI : 10.1016/S0031-3203(03)00122-5

URL : http://espace.library.uq.edu.au/view/UQ:9743/BABCSP.pdf

P. Armande, O. Montesinos, G. Monga, and . Vaysseix, Thin Nets Extraction Using a Multi-scale Approach, Computer Vision and Image Understanding, vol.73, issue.2, pp.248-257, 1999.
DOI : 10.1006/cviu.1998.0658

S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

B. Avants and J. Williams, An Adaptive Minimal Path Generation Technique for Vessel Tracking in CTA/CE-MRA Volume Images, Medical Imaging Computing and Computer- Assisted Intervention, pp.707-716, 2000.
DOI : 10.1007/978-3-540-40899-4_73

P. B. Bach, J. R. Jett, U. Pastorino, M. S. Tockman, S. J. Swensen et al., Computed Tomography Screening and Lung Cancer Outcomes, JAMA, vol.297, issue.9, pp.953-961, 2007.
DOI : 10.1001/jama.297.9.953

D. M. Bates and D. G. Watts, Nonlinear regression and its applications, 1988.

N. Becherer, H. Jödicke, G. Schlosser, J. Hesser, F. Zeilfelder et al., <title>On soft clipping of Zernike moments for deblurring and enhancement of optical point spread functions</title>, Computational Imaging IV
DOI : 10.1117/12.642272

P. N. Belhumeur, J. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.711-720, 1997.
DOI : 10.1109/34.598228

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

D. P. Bertsekas, Incremental Least Squares Methods and the Extended Kalman Filter, SIAM Journal on Optimization, vol.6, issue.3, pp.807-822, 1996.
DOI : 10.1137/S1052623494268522

J. Besag, On the statistical analysis of dirty images, Journal of Royal Statistics Society, vol.48, pp.259-302, 1986.

C. Beumier and M. Acheroy, Face verification from 3D and grey level clues, Pattern Recognition Letters, vol.22, issue.12, pp.1321-1329, 2001.
DOI : 10.1016/S0167-8655(01)00077-0

E. Bingham and A. Hyvärinen, A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS OF COMPLEX VALUED SIGNALS, International Journal of Neural Systems, vol.10, issue.01, pp.1-8, 2000.
DOI : 10.1142/S0129065700000028

R. M. Bolle and B. C. Vemuri, On three-dimensional surface reconstruction methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1-13, 1991.
DOI : 10.1109/34.67626

H. Bosch, S. Mitchell, B. Lelieveldt, F. Nijland, O. Kamp et al., Active Appearance???Motion Models for fully automated endocardial contour detection in time sequences of echocardiograms, CARS, pp.941-947, 2001.
DOI : 10.1016/S0531-5131(01)00159-5

J. Bosch, J. Reiber, G. Van-burken, J. Gerbrands, W. Gussenhoven et al., Automated endocardial contour detection in short-axis 2-D echocardiograms; methodology and assessment of variability, Proceedings. Computers in Cardiology 1988, pp.137-140, 1988.
DOI : 10.1109/CIC.1988.72584

J. G. Bosch, S. C. Mitchell, B. P. Lelieveldt, F. Nijland, O. Kamp et al., Automatic segmentation of echocardiographic sequences by active appearance motion models, IEEE Transactions on Medical Imaging, vol.21, issue.11, pp.1374-1383, 2002.
DOI : 10.1109/TMI.2002.806427

S. Bouix, K. Siddiqi, and A. R. Tannenbaum, Flux driven automatic centerline extraction, Medical Image Analysis, 2004.
DOI : 10.1016/j.media.2004.06.026

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.7686

Y. Boykov and M. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.105-112, 2001.
DOI : 10.1109/ICCV.2001.937505

Y. Boykov and K. Kolmogorov, Computing geodesics and minimal surfaces via graph cuts, Proceedings Ninth IEEE International Conference on Computer Vision
DOI : 10.1109/ICCV.2003.1238310

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.6433

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

W. Burgard, D. Fox, and D. Henning, Fast grid-based position tracking for mobile robots, KI '97: Proceedings of the 21st Annual German Conference on Artificial Intelligence, pp.289-300, 1997.
DOI : 10.1007/3540634932_23

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.120.4426

M. Butt and P. Maragos, Optimum design of chamfer distance transforms, IEEE Transactions on Image Processing, vol.7, issue.10, pp.1477-1484, 1998.
DOI : 10.1109/83.718487

R. Caldelli, A. Piva, M. Barni, and A. Carboni, Effectiveness of ST-DM Watermarking Against Intra-video Collusion, IWDW, pp.158-170, 2005.
DOI : 10.1007/11551492_13

C. Cañero and P. Radeva, Vesselness enhancement diffusion, Pattern Recognition Letters, vol.24, issue.16, pp.3141-3151, 2003.
DOI : 10.1016/j.patrec.2003.08.001

J. Cardoso, High-Order Contrasts for Independent Component Analysis, Neural Computation, vol.140, issue.1, pp.157-192, 1999.
DOI : 10.1109/78.599941

J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright et al., Reconstruction and representation of 3D objects with radial basis functions, Proceedings of the 28th annual conference on Computer graphics and interactive techniques , SIGGRAPH '01, pp.67-76, 2001.
DOI : 10.1145/383259.383266

V. Caselles, F. Catté, B. Coll, and F. Dibos, A geometric model for active contours in image processing, Numerische Mathematik, vol.36, issue.4, pp.1-31, 1993.
DOI : 10.1007/BF01385685

V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours, Proceedings of IEEE International Conference on Computer Vision, pp.694-699, 1995.
DOI : 10.1109/ICCV.1995.466871

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2196

T. Chan, B. Sandberg, and L. Vese, Active Contours without Edges for Vector-Valued Images, Journal of Visual Communication and Image Representation, vol.11, issue.2, pp.130-141, 2000.
DOI : 10.1006/jvci.1999.0442

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.6580

T. Chan and L. Vese, An Active Contour Model without Edges, International Conference on Scale-Space Theories in Computer Vision, pp.141-151, 1999.
DOI : 10.1007/3-540-48236-9_13

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.6740

Y. Chen, S. Thiruvenkadam, F. Huang, K. S. Gopinath, and R. W. Brigg, Simultaneous segmentation and registration for functional mr images, IARP International Conference on Pattern Recognition, p.10747, 2002.

D. Chopp, Computing Minimal Surfaces via Level Set Curvature Flow, Journal of Computational Physics, vol.106, issue.1, 1991.
DOI : 10.1006/jcph.1993.1092

L. Cohen, On active contour models and balloons, CVGIP: Image Understanding, vol.53, issue.2, pp.211-218, 1991.
DOI : 10.1016/1049-9660(91)90028-N

W. J. Cook, W. H. Cunningham, W. R. Pulleyblank, and A. Schrijver, Combinatorial optimization, 1998.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, issue.1, pp.38-59, 1995.
DOI : 10.1006/cviu.1995.1004

T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active appearance models, Lecture Notes in Computer Science, vol.1407, pp.484-498, 1998.
DOI : 10.1007/BFb0054760

T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, issue.1, pp.38-59, 1995.
DOI : 10.1006/cviu.1995.1004

D. Cremers, A variational framework for image segmentation combining motion estimation and shape regularization, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.53-58, 2003.
DOI : 10.1109/CVPR.2003.1211337

D. Cremers, Dynamical statistical shape priors for level set-based tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.8, pp.1262-1273, 2006.
DOI : 10.1109/TPAMI.2006.161

D. Cremers and G. Funka-lea, Time-variant statistical shape priors for level set based tracking, Workshop on VLSM, 2005.

D. Cremers, T. Kohlberger, and C. Schn-orr, Nonlinear Shape Statistics in Mumford???Shah Based Segmentation, European Conference on Computer Vision, pp.93-108, 2002.
DOI : 10.1007/3-540-47967-8_7

D. Cremers, N. A. Sochen, and C. Schnörr, Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling, Scale Space Methods in Computer Vision, pp.388-400, 2003.
DOI : 10.1007/3-540-44935-3_27

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.854

D. Cremers, F. Tischhauser, J. Weickert, and C. Schnorr, Diffusion snakes: Introducing statistical shape knowledge into the mumford-shah functional, International Journal of Computer Vision, vol.50, issue.3, pp.295-313, 2002.
DOI : 10.1023/A:1020826424915

B. Curless and M. Levoy, A volumetric method for building complex models from range images, Proceedings of the 23rd annual conference on Computer graphics and interactive techniques , SIGGRAPH '96, pp.303-312, 1996.
DOI : 10.1145/237170.237269

S. Dambreville, Y. Rathi, and A. Tannenbaum, Nonlinear shape prior from kernel space for geometric active contours, SPIE, 2006.

S. Dambreville, Y. Rathi, and A. Tannenbaum, Shape-Based Approach to Robust Image Segmentation using Kernel PCA, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.977-984, 2006.
DOI : 10.1109/CVPR.2006.279

G. B. Dantzig, Linear Programming and Extensions, 1993.
DOI : 10.1515/9781400884179

F. Daum and J. Huang, Curse of dimensionality and particle filters, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652), pp.1979-1993, 2003.
DOI : 10.1109/AERO.2003.1235126

M. De, L. Gorce, and N. Paragios, Monocular hand pose estimation using variable metric gradient descent, British Machine Vision Conference, p.1269, 2006.

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of Royal Statistics Society, vol.39, issue.1, pp.1-38, 1977.

H. Derin and H. Elliott, Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.39-55, 1987.
DOI : 10.1109/TPAMI.1987.4767871

H. Derin and H. Elliott, Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.39-55, 1987.
DOI : 10.1109/TPAMI.1987.4767871

S. Derrode, M. A. Chermi, and F. Ghorbel, Fourier-Based Invariant Shape Prior for Snakes, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1660289

URL : https://hal.archives-ouvertes.fr/hal-00082724

A. Dervieux and F. Thomasset, A finite element method for the simulation of a Rayleigh-Taylor instability, Lecture Notes in Mathematics, vol.216, pp.145-159, 1979.
DOI : 10.1016/0021-9991(79)90086-X

A. Dervieux and F. Thomasset, Multifluid incompressible flows by a finite element method, Seventh International Conference on Numerical Methods in Fluid Dynamics, pp.158-163, 1980.
DOI : 10.1007/3-540-10694-4_22

T. Deschamps, Curve and Shape Extraction with Minimal Path and Level-Sets techniques - Applications to 3D Medical Imaging, p.75775, 2001.

T. Deschamps and L. D. Cohen, Fast extraction of minimal paths in 3D images and applications to virtual endoscopy11A preliminary version of this work was presented at the ECCV???2000 Conference., Medical Image Analysis, vol.5, issue.4, pp.281-299, 2001.
DOI : 10.1016/S1361-8415(01)00046-9

T. Deschamps and L. D. Cohen, Fast extraction of tubular and tree 3D surfaces with front propagation methods, Object recognition supported by user interaction for service robots, 2002.
DOI : 10.1109/ICPR.2002.1044862

M. Descoteaux, L. Collins, and K. Siddiqi, Geometric Flows for Segmenting Vasculature in MRI: Theory and Validation, Medical Imaging Computing and Computer-Assisted Intervention, pp.500-507, 2004.
DOI : 10.1007/978-3-540-30135-6_61

E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische Mathematik, vol.4, issue.1, pp.269-271, 1959.
DOI : 10.1007/BF01386390

H. Q. Dinh, G. Turk, and G. Slabaugh, Reconstructing surfaces by volumetric regularization using radial basis functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.10, pp.1358-1371, 2002.
DOI : 10.1109/TPAMI.2002.1039207

G. Doblinger, An adaptive Kalman filter for the enhancement of noisy AR signals, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187), pp.1-4, 1998.
DOI : 10.1109/ISCAS.1998.694474

A. J. Dobson, Introduction to Generalized Linear Models, Second Edition, 2001.
DOI : 10.1201/9781420057683

A. Doucet, J. De-freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

R. Duda and P. Hart, Pattern Classification and Scene Analysis, 1973.

P. Fearnhead and P. Clifford, On-line inference for hidden Markov models via particle filters, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.3, issue.4, pp.887-899, 2003.
DOI : 10.1016/0005-1098(82)90012-7

S. D. Fenster, C. G. Kuo, and J. R. Kender, Nonparametric training of snakes to find indistinct boundaries, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 2001.
DOI : 10.1109/MMBIA.2001.991709

M. Figueiredo and J. Leitao, A nonsmoothing approach to the estimation of vessel contours in angiograms, IEEE Transactions on Medical Imaging, vol.14, issue.1, pp.162-172, 1995.
DOI : 10.1109/42.370413

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

A. Fitzgibbon, Robust registration of 2D and 3D point sets, British Machine Vision Conference, pp.411-420, 2001.
DOI : 10.1016/j.imavis.2003.09.004

C. Florin, N. Paragios, and J. Williams, Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries, Medical Imaging Computing and Computer-Assisted Intervention, pp.246-253, 2005.
DOI : 10.1007/11566465_31

C. Florin, N. Paragios, and J. Williams, Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation, European Conference on Computer Vision, pp.476-489, 2006.
DOI : 10.1007/11744078_37

URL : https://hal.archives-ouvertes.fr/hal-00116332

A. Frangi, W. Niessen, P. Nederkoorn, O. Elgersma, and M. Viergever, Three-dimensional model-based stenosis quantification of the carotid arteries from contrast-enhanced MR angiography, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737), pp.110-118, 2000.
DOI : 10.1109/MMBIA.2000.852367

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, Multiscale vessel enhancement filtering, Lecture Notes in Computer Science, vol.191, issue.6, p.1496, 1998.
DOI : 10.1148/radiology.191.1.8134563

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.7722

S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.721-741, 1984.

C. A. Glasbey and K. V. Mardia, A review of image-warping methods, Journal of Applied Statistics, vol.25, issue.2, pp.155-171, 1998.
DOI : 10.1080/02664769823151

S. H. Godsill and P. J. Rayner, Digital Audio Restoration: A Statistical Model Based Approach, 1998.

N. Gordon, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEEE Proceedings of Radar and Signal Processing, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

N. Gordon, On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, Journal of Statistics and Computing, vol.10, pp.197-208, 2000.

U. Grenander, Y. Chow, and D. M. Keenan, Hands: a pattern theoretic study of biological shapes, 1991.
DOI : 10.1007/978-1-4612-3046-5

P. Hall and R. Martin, Incremental Eigenanalysis for Classification, Procedings of the British Machine Vision Conference 1998, pp.286-295, 1998.
DOI : 10.5244/C.12.29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.4801

G. Hamarneh and C. Mcintosh, Deformable organisms for medical image analysis. Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery, pp.387-443, 2007.
DOI : 10.1007/978-0-387-68413-0_12

M. H. Hansen and B. Yu, Model Selection and the Principle of Minimum Description Length, Journal of the American Statistical Association, vol.96, issue.454, pp.746-774, 2001.
DOI : 10.1198/016214501753168398

M. Hart and L. Holley, A method of Automated Coronary Artery Trackin in Unsubtracted Angiograms, IEEE Computers in Cardiology, pp.93-96, 1993.

T. Heimann, I. Wolf, and H. Meinzer, Active Shape Models for a Fully Automated 3D Segmentation of the Liver ??? An Evaluation on Clinical Data, Medical Imaging Computing and Computer-Assisted Intervention, pp.41-48, 2006.
DOI : 10.1007/11866763_6

L. Hermoye, I. Laamari-azjal, Z. Cao, L. Annet, J. Lerut et al., Liver Segmentation in Living Liver Transplant Donors: Comparison of Semiautomatic and Manual Methods, Radiology, vol.234, issue.1, pp.171-178, 2005.
DOI : 10.1148/radiol.2341031801

A. Hilton, A. J. Stoddart, J. Illingworth, and T. Windeatt, Reliable surface reconstruction from multiple range images, European Conference on Computer Vision, pp.117-126, 1996.
DOI : 10.1007/BFb0015528

URL : http://epubs.surrey.ac.uk/111037/2/hilton96eccv.pdf

H. Hoppe, T. Derose, T. Duchamp, J. Mcdonald, and W. Stuetzle, Surface reconstruction from unorganized points, SIGGRAPH '92: Proceedings of the 19th annual conference on Computer graphics and interactive techniques, pp.71-78, 1992.
DOI : 10.1145/133994.134011

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.4735

H. Hoppe, T. Derose, T. Duchamp, J. Mcdonald, and W. Stuetzle, Surface reconstruction from unorganized points, SIGGRAPH '92: Proceedings of the 19th annual conference on Computer graphics and interactive techniques, pp.71-78, 1992.

B. Horn and B. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.1651

W. Hu, T. Tan, L. Wang, and S. Maybank, A Survey on Visual Surveillance of Object Motion and Behaviors, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.34, issue.3, pp.334-352, 2004.
DOI : 10.1109/TSMCC.2004.829274

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

S. Ioffe, Probabilistic Linear Discriminant Analysis, European Conference on Computer Vision, pp.531-542, 2006.
DOI : 10.1007/11744085_41

M. Isard and A. Blake, Contour tracking by stochastic propagation of conditional density, European Conference on Computer Vision, pp.343-356, 1996.
DOI : 10.1007/BFb0015549

M. Isard and A. Blake, Condensation?conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998.
DOI : 10.1023/A:1008078328650

J. Kotecha and P. Djuric, Gaussian sum particle filtering, IEEE Transactions on Signal Processing, vol.51, issue.10, pp.2602-2612, 2003.
DOI : 10.1109/TSP.2003.816754

M. Jolly, Assisted Ejection Fraction in B-Mode and Contrast Echocardiography, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.97-100, 2006.
DOI : 10.1109/ISBI.2006.1624861

S. Joshi and M. Miller, Landmark matching via large deformation diffeomorphisms, IEEE Transactions on Image Processing, vol.9, issue.8, pp.1357-1370, 2000.
DOI : 10.1109/83.855431

O. Juan, R. Keriven, and G. Postelnicu, Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours, International Journal of Computer Vision, vol.21, issue.2, pp.7-25, 2006.
DOI : 10.1007/s11263-006-6849-5

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, IEEE International Conference in Computer Vision, pp.261-268, 1987.
DOI : 10.1007/BF00133570

S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Gradient flows and geometric active contour models, Proceedings of IEEE International Conference on Computer Vision, pp.810-815, 1995.
DOI : 10.1109/ICCV.1995.466855

URL : https://hal.archives-ouvertes.fr/hal-00002665

S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Conformal curvature flows: from phase transitions to active vision. Archive for Rational Mechanics and Analysis, pp.275-301, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00002650

S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Geometric active contours for segmentation of medical imagery, IEEE Transactions on Medical Imaging, vol.16, pp.199-209, 1997.

R. Knothe, S. Romdhani, and T. Vetter, Combining PCA and LFA for Surface Reconstruction from a Sparse Set of Control Points, 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp.637-644, 2006.
DOI : 10.1109/FGR.2006.31

V. Kolmogorov and R. Zabih, Multi-camera Scene Reconstruction via Graph Cuts, European Conference on Computer Vision, pp.82-96, 2002.
DOI : 10.1007/3-540-47977-5_6

D. Kovacevic and S. Loncaric, Radial basis function-based image segmentation using a receptive field, Proceedings of Computer Based Medical Systems, p.126, 1997.
DOI : 10.1109/CBMS.1997.596421

K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset, Model-based multiscale detection of 3D vessels, IEEE Conference on Computer Vision and Pattern Recognition, pp.722-727, 1998.
URL : https://hal.archives-ouvertes.fr/inria-00073248

I. Kunttu, L. Lepisto, J. Rauhamaa, and A. Visa, Multiscale Fourier descriptor for shape classification, 12th International Conference on Image Analysis and Processing, 2003.Proceedings., p.536, 2003.
DOI : 10.1109/ICIAP.2003.1234105

V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, Graphcut textures, ACM Transactions on Graphics, vol.22, issue.3, pp.277-286, 2003.
DOI : 10.1145/882262.882264

M. Leventon, E. Grimson, and O. Faugeras, Statistical Shape Influence in Geodesic Active Controus, IEEE Conference on Computer Vision and Pattern Recognition, pp.316-322, 2000.

Y. Li, On incremental and robust subspace learning, Pattern Recognition, vol.37, issue.7, pp.1509-1518, 2004.
DOI : 10.1016/j.patcog.2003.11.010

C. Liao and G. Medioni, Surface Approximation of a Cloud of 3D Points, Graphical Models and Image Processing, vol.57, issue.1, pp.67-74, 1995.
DOI : 10.1006/gmip.1995.1007

C. Liu and N. Ahuja, A model for dynamic shape and its applications, IEEE Conference on Computer Vision and Pattern Recognition, pp.129-134, 2004.

F. Liu, B. Zhao, P. Kijewski, L. Wang, and L. Schwartz, Liver segmentation for CT images using GVF snake, Medical Physics, vol.16, issue.12, pp.3699-3706, 2005.
DOI : 10.1109/42.640755

W. Lorensen and H. Cline, Marching cubes: A high resolution 3D surface construction algorithm, SIGGRAPH '87: Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp.163-169, 1987.

W. Ma, F. Wu, and M. Ouhyoung, Skeleton extraction of 3d objects with radial basis functions, SMI '03: Proceedings of the Shape Modeling International 2003, p.207, 2003.

J. Maccormick and A. Blake, A probabilistic exclusion principle for tracking multiple objects, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.57-71, 2000.
DOI : 10.1109/ICCV.1999.791275

J. Maintz and M. Viergever, A survey of medical image registration, Medical Image Analysis, vol.2, issue.1, pp.1-36, 1998.
DOI : 10.1016/S1361-8415(01)80026-8

R. Malladi and J. Sethian, A real-time algorithm for medical shape recovery, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp.304-310, 1998.
DOI : 10.1109/ICCV.1998.710735

R. Malladi, J. Sethian, and B. Vemuri, A topology independent shape modeling scheme, Geometric Methods in Computer Vision II, pp.246-256, 1993.
DOI : 10.1117/12.146630

R. Malladi, J. Sethian, and B. Vemuri, Shape modeling with front propagation: a level set approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.2, pp.158-175, 1995.
DOI : 10.1109/34.368173

H. Meinzer, M. Thorn, and C. Cardenas, Computerized planning of liver surgery???an overview, Computers & Graphics, vol.26, issue.4, pp.569-576, 2002.
DOI : 10.1016/S0097-8493(02)00102-4

D. Nain, A. Yezzi, and G. Turk, Vessel Segmentation Using a Shape Driven Flow, Medical Imaging Computing and Computer-Assisted Intervention, 2004.
DOI : 10.1007/978-3-540-30135-6_7

J. C. Nascimento, J. S. Marques, and J. M. Sanches, Estimation of cardiac phases in echographic images using multiple models, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), pp.149-152, 2003.
DOI : 10.1109/ICIP.2003.1246638

J. A. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

A. Neumaier and T. Schneider, Estimation of parameters and eigenmodes of multivariate autoregressive models, ACM Transactions on Mathematical Software, vol.27, issue.1, pp.27-57, 2001.
DOI : 10.1145/382043.382304

M. Novotni and R. Klein, Shape retrieval using 3D Zernike descriptors, Computer-Aided Design, vol.36, issue.11, pp.1047-1062, 2004.
DOI : 10.1016/j.cad.2004.01.005

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.8238

T. O´donnello´donnell, T. Boult, X. Fang, and A. Gupta, The Extruded Generalized Cylider: A Deformable Model for Object Recovery, IEEE Conference on Computer Vision and Pattern Recognition, pp.174-181, 1994.

K. Okuma, A. Taleghani, N. De-freitas, J. Little, and D. Lowe, A Boosted Particle Filter: Multitarget Detection and Tracking, European Conference on Computer Vision, pp.28-39, 2004.
DOI : 10.1007/978-3-540-24670-1_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.3138

J. O. Rourke, Computational geometry in C, 1994.

S. Osher and N. Paragios, Geometric Level Set Methods in Imaging, Vision and Graphics, 2003.

S. Osher and J. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988.
DOI : 10.1016/0021-9991(88)90002-2

C. Otto, The Practice of Clinical Echocardiography, 2002.

N. Thrasyvoulos, N. S. Pappas, and . Jayant, An adaptive clustering algorithm for image segmentation, Second International Conference on Computer Vision, pp.310-315, 1988.

N. Paragios, Shape-based Segmentation and Tracking in Cardiac Image Analysis, IEEE Transactions on Medical Imaging, pp.773-776, 2003.

N. Paragios, Y. Chen, and O. Faugeras, Handbook of Mathematical Models in Computer Vision, 2005.
DOI : 10.1007/0-387-28831-7

N. Paragios and R. Deriche, Geodesic active regions for motion estimation and tracking, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.688-674, 1999.
DOI : 10.1109/ICCV.1999.791292

URL : https://hal.archives-ouvertes.fr/inria-00073043

N. Paragios and R. Deriche, Geodesic active contours and level sets for the detection and tracking of moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.3, pp.266-280, 2000.
DOI : 10.1109/34.841758

N. Paragios and R. Deriche, Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision, Journal of Visual Communication and Image Representation, vol.13, issue.1-2, pp.249-268, 2002.
DOI : 10.1006/jvci.2001.0475

N. Paragios and R. Deriche, Geodesic Active Regions Level Set Methods for Supervised Texture Segmentation, International Journal of Computer Vision, vol.46, issue.3, pp.223-247, 2002.
DOI : 10.1023/A:1014080923068

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.2737

N. Paragios, M. Rousson, and V. Ramesh, Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration, European Conference on Computer Vision, pages II, pp.775-790, 2002.
DOI : 10.1007/3-540-47967-8_52

H. Park, P. Bland, and C. Meyer, Construction of an abdominal probabilistic atlas and its application in segmentation, IEEE Transactions on Medical Imaging, vol.22, issue.4, pp.483-492, 2003.
DOI : 10.1109/TMI.2003.809139

R. Petrocelli, K. Manbeck, and J. Elion, Three Dimentional Structue Recognition in Digital Angiograms using Gauss-Markov Models, IEEE Computers in Radiology, pp.101-104, 1993.

B. Pham, Quadratic B-splines for automatic curve and surface fitting, Computers & Graphics, vol.13, issue.4, pp.471-475, 1989.
DOI : 10.1016/0097-8493(89)90008-3

P. Quelhas and J. Boyce, Vessel Segmentation and Branching Detection Using an Adaptive Profile Kalman Filter in Retinal Blood Vessel Structure Analysis, Pattern Recognition and Image Analysis: First Iberian Conference, pp.802-809, 2003.
DOI : 10.1007/978-3-540-44871-6_93

Y. Rathi, N. Vaswani, A. Tannenbaum, and A. Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.2-9, 2005.
DOI : 10.1109/CVPR.2005.271

B. Reitinger, A. Bornik, R. Beichel, and D. Schmalstieg, Liver Surgery Planning Using Virtual Reality, IEEE Computer Graphics and Applications, vol.26, issue.6, pp.36-47, 2006.
DOI : 10.1109/MCG.2006.131

K. Rohr, Landmark-Based Image Analysis: Using Geometric and Intensity Models, 2001.
DOI : 10.1007/978-94-015-9787-6

S. Roth, L. Sigal, and M. Black, Gibbs likelihoods for Bayesian tracking, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.886-893, 2004.
DOI : 10.1109/CVPR.2004.1315125

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.2766

M. Rousson and D. Cremers, Efficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation, Medical Imaging Computing and Computer-Assisted Intervention, pp.757-764, 2005.
DOI : 10.1007/11566489_93

M. Rousson and N. Paragios, Shape Priors for Level Set Representations, European Conference on Computer Vision, pages II, pp.78-93, 2002.
DOI : 10.1007/3-540-47967-8_6

M. Rousson and N. Paragios, Prior Knowledge, Level Set Representations & Visual Grouping, International Journal of Computer Vision, vol.18, issue.3, 2007.
DOI : 10.1007/s11263-007-0054-z

URL : https://hal.archives-ouvertes.fr/hal-00918700

D. Rowe, I. Rius, J. Gonzàlez, and J. Villanueva, Robust Particle Filtering for Object Tracking, International Conference on Image Analysis and Processing, pp.1158-1165, 2005.
DOI : 10.1007/11553595_142

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.489.9324

S. Roy, Stereo without epipolar lines: A maximum-flow formulation, International Journal of Computer Vision, vol.34, issue.2/3, pp.147-161, 1999.
DOI : 10.1023/A:1008192004934

F. L. Ruberg, Computed Tomography of the Coronary Arteries, Circulation, vol.112, issue.1, 2005.
DOI : 10.1161/CIRCULATIONAHA.105.553347

D. Rueckert, P. Burger, S. Forbat, R. Mohiadin, and G. Yang, Automatic tracking of the aorta in cardiovascular MR images using deformable models, IEEE Transactions on Medical Imaging, vol.16, issue.5, pp.581-590, 1997.
DOI : 10.1109/42.640747

M. Rutishauser, M. Stricker, and M. Trobina, Merging range images of arbitrarily shaped objects, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, pp.573-580, 1994.
DOI : 10.1109/CVPR.1994.323797

Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig et al., 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery, pp.213-222, 1997.
DOI : 10.1007/BFb0029240

A. Schenk, G. Prause, and H. Peitgen, Efficient Semiautomatic Segmentation of 3D??Objects??in??Medical??Images, Medical Imaging Computing and Computer-Assisted Intervention, pp.186-195, 2000.
DOI : 10.1007/978-3-540-40899-4_19

P. Schoenhagen, A. E. Stillman, S. S. Halliburton, S. A. Kuzmiak, T. Painter et al., Non-invasive coronary angiography with multi-detector computed tomography: comparison to conventional X-ray angiography, The International Journal of Cardiovascular Imaging, vol.90, issue.3, pp.63-72, 2005.
DOI : 10.1007/s10554-004-1887-y

K. Seo, H. Kim, T. Park, P. Kim, and J. Park, Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing, ICNC, pp.1027-1030, 2005.
DOI : 10.1007/11539087_135

J. Sethian, A Review of the Theory, Algorithms, and Applications of Level Set Methods for Propagating Interfaces, pp.487-499, 1995.

J. Sethian, Level Set Methods, 1996.

G. K. Shaffer and R. Pito, Mesh integration based on co-measurements, IEEE International Conference on Image Processing, pp.397-400, 1996.

J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, pp.888-905, 2000.

L. Soler, H. Delingette, G. Malandain, J. Montagnat, N. Ayache et al., Fully automatic anatomical, pathological , and functional segmentation from CT scans for hepatic surgery, Computer Aided Surgery (CAS), vol.6, issue.3, 2001.
URL : https://hal.archives-ouvertes.fr/inria-00615108

E. Sorantin, C. Halmai, B. Erbohelyi, K. Palagyi, K. Nyul et al., Spiral-CT-based assessment of tracheal stenoses using 3-D-skeletonization, IEEE Transactions on Medical Imaging, vol.21, issue.3, pp.263-273, 2002.
DOI : 10.1109/42.996344

M. Soucy and D. Laurendeau, A dynamic integration algorithm to model surfaces from multiple range views, Machine Vision and Applications, pp.53-62, 1995.
DOI : 10.1007/BF01213638

R. Strand and G. Borgefors, Distance transforms for three-dimensional grids with non-cubic voxels, Computer Vision and Image Understanding, vol.100, issue.3, pp.294-311, 2005.
DOI : 10.1016/j.cviu.2005.04.006

J. Suri, K. Liu, L. Reden, and S. Laxminarayan, A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II, IEEE Transactions on Information Technology in Biomedicine, vol.6, issue.4, pp.338-350, 2002.
DOI : 10.1109/TITB.2002.804136

M. R. Teague, Image analysis via the general theory of moments*, Journal of the Optical Society of America, vol.70, issue.8, pp.920-930, 1979.
DOI : 10.1364/JOSA.70.000920

S. Tehrani, T. E. Weymouth, and B. Schunck, Interpolating cubic spline contours by minimizing second derivative discontinuity, [1990] Proceedings Third International Conference on Computer Vision, pp.713-716
DOI : 10.1109/ICCV.1990.139624

D. Terzopoulos and R. Szeliski, Tracking with Kalman Snakes, Active Vision, pp.3-20, 1992.

P. Thévenaz, T. Blu, and M. Unser, Image interpolation and resampling, Handbook of Medical Imaging, Processing and Analysis, chapter 25, pp.393-420, 2000.

A. Tonazzini and L. Bedini, Monte Carlo Markov chain techniques for unsupervised MRF-based image denoising, Pattern Recognition Letters, vol.24, issue.1-3, pp.55-64, 2003.
DOI : 10.1016/S0167-8655(02)00188-5

URL : http://puma.isti.cnr.it/rmydownload.php?filename=cnr.isti/cnr.iei/2001-TR-023/2001-TR-023_.pdf

K. Toyama and A. Blake, Probabilistic tracking in a metric space, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.50-59, 2001.
DOI : 10.1109/ICCV.2001.937599

J. Tsitsiklis, Efficient algorithms for globally optimal trajectories, IEEE Transactions on Automatic Control, vol.40, issue.9, pp.1528-1538, 1995.
DOI : 10.1109/9.412624

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.8514

G. Turk and M. Levoy, Zippered polygon meshes from range images, Proceedings of the 21st annual conference on Computer graphics and interactive techniques , SIGGRAPH '94, pp.311-318, 1994.
DOI : 10.1145/192161.192241

G. Turk and J. O-'brien, Modelling with implicit surfaces that interpolate, ACM Transactions on Graphics, vol.21, issue.4, pp.855-873, 2002.
DOI : 10.1145/571647.571650

M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol.10, issue.9, pp.71-86, 1991.
DOI : 10.1007/BF00239352

M. Uzumcu, A. F. Frangi, J. Reiber, and B. Lelieveldt, The use of independent component analysis in statistical shape models, pp.375-83, 2003.

A. Vasilevskiy and K. Siddiqi, Flux Maximizing Geometric Flows, IEEE International Conference in Computer Vision, pp.149-154, 2001.

O. Veksler, Image segmentation by nested cuts, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), p.1339, 2000.
DOI : 10.1109/CVPR.2000.855838

E. Wan and R. Van-der-merwe, The Unscented Kalman Filter, Kalman Filtering and Neural Networks, chapter 7, 2001.
DOI : 10.1002/0471221546.ch7

X. Wang and H. Wang, Evolutionary gibbs sampler for image segmentation, IEEE International Conference on Image Processing, pp.3479-3482, 2004.

Y. Wang and L. H. Staib, Boundary finding with correspondence using statistical shape models, IEEE Conference on Computer Vision and Pattern Recognition, 1998.

C. Wee, R. Paramesran, and F. Takeda, New computational methods for full and subset Zernike moments, Information Sciences, vol.159, issue.3-4, pp.3-4203, 2004.
DOI : 10.1016/j.ins.2003.08.006

G. Welch and G. Bishop, An introduction to the kalman filter, 2004.

W. West, Modelling with mixtures, 1993.

O. Wink, W. J. Niessen, and M. A. Viergever, Multiscale Vessel Tracking, IEEE Transactions on Medical Imaging, vol.23, issue.1, pp.130-133, 2004.
DOI : 10.1109/TMI.2003.819920

Z. Wu and R. Leahy, An optimal graph theoretic approach to data clustering: theory and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.11, pp.1101-1113, 1993.
DOI : 10.1109/34.244673

A. Yezzi, A. Tsai, and A. Willsky, A statistical approach to snakes for bimodal and trimodal imagery, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.898-903, 1999.
DOI : 10.1109/ICCV.1999.790317

P. Yim, P. Choyke, and R. Summers, Gray-scale skeletonization of small vessels in magnetic resonance angiography, IEEE Transactions on Medical Imaging, vol.19, issue.6, pp.568-576, 2000.
DOI : 10.1109/42.870662

L. Younes, Combining geodesic interpolating splines and affine transformations, IEEE Transactions on Image Processing, vol.15, issue.5, pp.1111-1119, 2006.
DOI : 10.1109/TIP.2005.864163

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.5406

D. S. Zhang and G. Lu, A comparative study of curvature scale space and fourier descriptors, Journal of Visual Communication and Image Representation, vol.14, issue.1, pp.41-60, 2003.

T. Zhang and D. Freedman, Tracking objects using density matching and shape priors, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1056-1062, 2003.
DOI : 10.1109/ICCV.2003.1238466

S. Zhu and A. Yuille, Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation, Proceedings of IEEE International Conference on Computer Vision, pp.884-900, 1996.
DOI : 10.1109/ICCV.1995.466909

S. C. Zhu and D. Mumford, Prior learning and gibbs reaction-diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.11, pp.1236-1250, 1997.

L. Zöllei, W. E. Grimson, A. Norbash, and W. M. Wells, 2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.696-703, 2001.
DOI : 10.1109/CVPR.2001.991032