M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Electronic Imaging, vol.13, pp.146-165, 2004.

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.285-296, 1975.
DOI : 10.1109/TSMC.1979.4310076

Y. Bazi, L. Bruzzone, and F. Melgani, Image thresholding based on the EM algorithm and the generalized Gaussian distribution, Pattern Recognition, vol.40, issue.2, pp.619-634, 2007.
DOI : 10.1016/j.patcog.2006.05.006

K. Hammouche, M. Diaf, and P. Siarry, A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation, Computer Vision and Image Understanding, vol.109, issue.2, pp.163-175, 2008.
DOI : 10.1016/j.cviu.2007.09.001
URL : https://hal.archives-ouvertes.fr/hal-00916870

A. Nakib, H. Oulhadj, and P. Siarry, Fractional differentiation and non-Pareto multiobjective optimization for image thresholding, Engineering Applications of Artificial Intelligence, vol.22, issue.2, pp.236-249, 2009.
DOI : 10.1016/j.engappai.2008.07.005
URL : https://hal.archives-ouvertes.fr/hal-00916860

A. Nakib, H. Oulhadj, and P. Siarry, Image histogram thresholding based on multiobjective optimization, Signal Processing, vol.87, issue.11, pp.2516-2534, 2007.
DOI : 10.1016/j.sigpro.2007.04.001

M. N. Ahmed and A. A. Farag, Two-stage neural network for volume segmentation of medical images, Pattern Recognition Letters, vol.18, issue.11-13, pp.1143-1151, 1997.
DOI : 10.1016/S0167-8655(97)00091-3

C. Y. Chang and P. C. Chung, Medical image segmentation using a contextual-constraint-based Hopfield neural cube, Image and Vision Computing, vol.19, issue.9-10, pp.669-678, 2001.
DOI : 10.1016/S0262-8856(01)00039-7

M. N. Kurnaz, Z. Dokur, and T. Olmez, An incremental neural network for tissue segmentation in ultrasound images, Computer Methods and Programs in Biomedicine, vol.85, issue.3, pp.187-195, 2007.
DOI : 10.1016/j.cmpb.2006.10.010

F. Kurugollu, B. Sankur, and A. Harmanci, Image segmentation by relaxation using constraint satisfaction neural network. Image & Vision Comp, pp.483-497, 2002.
DOI : 10.1016/s0262-8856(02)00023-9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.2367

N. Sharma, A. K. Ray, S. Sharma, . Shukla, . Kk et al., Segmentation and classification of medical images using textureprimitive features: Application of BAM-type artificial neural network, Medical physics/Association of Medical Physicists of India, vol.33, p.119, 2008.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.4065

M. Bicego, A. Lagorio, E. Grosso, and M. Tistarelli, On the Use of SIFT Features for Face Authentication, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), p.35, 2006.
DOI : 10.1109/CVPRW.2006.149

J. H. Yun and R. H. Park, Self-calibration with two views using the scale-invariant feature transform Advances in Visual Computing, pp.589-598, 2006.

T. Lindeberg, Feature Detection with Automatic Scale Selection, International Journal of Computer Vision, vol.30, issue.2, pp.79-116, 1998.
DOI : 10.1023/A:1008045108935

C. Y. Tang, Y. L. Wu, M. K. Hor, and W. H. Wang, Modified SIFT descriptor for image matching under interference, In: ICMLC, vol.6, pp.3294-3300, 2008.

R. Lemuz-lópez and M. Arias-estrada, Iterative Closest SIFT Formulation for Robust Feature Matching, Advances in Visual Computing, pp.502-513, 2006.
DOI : 10.1007/11919629_51

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

S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1998.
DOI : 10.1016/0021-9991(88)90002-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.5254

C. Li, C. Xu, C. Gui, and M. D. Fox, Level Set Evolution Without Re-initialization: A New Variational Formulation, Computer Vision and Pattern Recognition, 2005.

Y. Zhang, B. J. Matuszewski, L. K. Shark, and C. J. Moore, Medical Image Segmentation Using New Hybrid Level-Set Method, 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics, pp.71-76, 2008.
DOI : 10.1109/MediVis.2008.12

B. Liu, H. Cheng, J. Huang, J. Tian, X. Tang et al., Probability density difference-based active contour for ultrasound image segmentation, Pattern Recognition, vol.43, issue.6, pp.2028-2042, 2010.
DOI : 10.1016/j.patcog.2010.01.002