Y. Xu, J. Zhu, E. Chang, and Z. Tu, Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.964-971, 2012.

E. Kim, H. Li, and X. Huang, A hierarchical image clustering cosegmentation framework, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.686-693, 2012.
DOI : 10.1109/CVPR.2012.6247737
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.2998

M. B. Salah, I. B. Ayed, and A. Mitiche, Active Curve Recovery of Region Boundary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.5, pp.834-849, 2012.
DOI : 10.1109/TPAMI.2011.201

K. Zhang, L. Zhang, and S. Zhang, A variational multiphase level set approach to simultaneous segmentation and bias correction, 2010 IEEE International Conference on Image Processing, pp.4105-4108, 2010.
DOI : 10.1109/ICIP.2010.5651554

S. Balla-arabé, X. Gao, and B. Wang, A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method, IEEE Transactions on Cybernetics, vol.43, issue.3, pp.910-920, 2013.
DOI : 10.1109/TSMCB.2012.2218233

C. Li, R. Huang, Z. Ding, J. Chris, D. N. Metaxas et al., Gore: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Transactions on Image Processing, vol.20, issue.7, pp.2007-2016, 2011.

V. A. Prisacariu and I. Reid, Nonlinear shape manifolds as shape priors in level set segmentation and tracking, CVPR 2011, pp.2185-2192, 2011.
DOI : 10.1109/CVPR.2011.5995687
URL : https://digital.library.adelaide.edu.au/dspace/retrieve/148967/RA_hdl_84208.pdf

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.5254

S. Balla-arabé and X. Gao, Image multi-thresholding by combining the lattice Boltzmann model and a localized level set algorithm, Neurocomputing, vol.93, pp.106-114, 2012.
DOI : 10.1016/j.neucom.2012.04.019

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988.
DOI : 10.1007/BF00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318

X. Gao, B. Wang, D. Tao, and X. Li, A relay level set method for automatic image segmentation, IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol.41, issue.2, pp.518-525, 2011.

R. Malladi, J. Sethian, and B. Vemuri, A topology independent shape modeling scheme, Proceedings of SPIE Conference on Geometric Methods in Computer Vision II, pp.246-258, 1993.
DOI : 10.1117/12.146630

V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours, Proceedings of IEEE International Conference on Computer Vision, pp.61-79, 1997.
DOI : 10.1109/ICCV.1995.466871
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2196

N. Paragios and R. Deriche, Geodesic active contours for supervised texture segmentation, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp.1034-1040, 1999.
DOI : 10.1109/CVPR.1999.784715

T. Chan and L. Vese, Active contours without edges, IEEE Transactions on Image Processing, vol.10, issue.2, pp.266-277, 2001.
DOI : 10.1109/83.902291
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.1828

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989.
DOI : 10.1002/cpa.3160420503
URL : http://nrs.harvard.edu/urn-3:HUL.InstRepos:3637121

S. Osher and R. Fedkiw, Level set methods and dynamic implicit surfaces, 2003.
DOI : 10.1007/b98879
URL : http://dx.doi.org/10.1016/s0898-1221(03)90179-9

S. Balla-arabé, B. Wang, and X. Gao, Level Set Region Based Image Segmentation Using Lattice Boltzmann Method, 2011 Seventh International Conference on Computational Intelligence and Security, pp.1159-1163, 2011.
DOI : 10.1109/CIS.2011.257

X. He and L. Luo, Lattice Boltzmann Model for the Incompressible Navier???Stokes Equation, Journal of Statistical Physics, vol.88, issue.3/4, pp.927-944, 1997.
DOI : 10.1023/B:JOSS.0000015179.12689.e4

Y. Zhao, Lattice Boltzmann based PDE solver on the GPU. The Visual Computer, pp.323-333, 2007.

Y. Chen, Z. Yan, and Y. Chu, Cellular Automata based Level Set Method for Image Segmentation, 2007 IEEE/ICME International Conference on Complex Medical Engineering, pp.23-27, 2007.
DOI : 10.1109/ICCME.2007.4381715

C. Li, C. Xu, C. Gui, and M. Fox, Distance regularized level set evolution and its application to image segmentation, IEEE Transactions on Image Processing, vol.19, issue.12, pp.3243-3254, 2010.

A. Hagan and Y. Zhao, Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster, Part II, pp.960-969, 2009.
DOI : 10.1007/978-3-642-10520-3_92

M. D. Levine and A. M. Nazif, Dynamic Measurement of Computer Generated Image Segmentations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.7, issue.2, pp.155-164, 1985.
DOI : 10.1109/TPAMI.1985.4767640

Y. Boycov, 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

J. Malik, S. Belongie, T. Leung, and J. Shi, Contour and Texture Analysis for Image Segmentation, International Journal of Computer Vision, vol.43, issue.1, pp.7-27, 2001.
DOI : 10.1007/978-1-4615-4413-5_9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.1476

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

C. Brun, N. Leporé, X. Pennec, Y. Chou, and A. Lee, A Nonconservative Lagrangian Framework for Statistical Fluid Registration—SAFIRA, IEEE Transactions on Medical Imaging, vol.30, issue.2, pp.184-202, 2011.
DOI : 10.1109/TMI.2010.2067451

A. Nakhmani and A. Tannenbaum, Self-Crossing Detection and Location for Parametric Active Contours, IEEE Transactions on Image Processing, vol.21, issue.7, pp.3150-3156, 2012.
DOI : 10.1109/TIP.2012.2188808
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660981

G. Charpiat, O. Faugeras, and R. Keriven, Shape Statistics for Image Segmentation with Prior, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-6, 2007.
DOI : 10.1109/CVPR.2007.383009

N. Paragios and M. Rousson, Shape Priors for Level Set Representation, IEEE European Conference on Computer Vision (ECCV), pp. II, pp.78-93, 2002.

C. Benedek, X. Descombes, and J. Zerubia, Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.1, pp.33-50, 2012.
DOI : 10.1109/TPAMI.2011.94
URL : https://hal.archives-ouvertes.fr/hal-00730552

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour Detection and Hierarchical Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.5, pp.898-916, 2011.
DOI : 10.1109/TPAMI.2010.161

Y. Yang, S. Hallman, D. Ramanan, and C. Fowlkes, Layered Object Models for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.9, pp.1731-1743, 2012.
DOI : 10.1109/TPAMI.2011.208