M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysis, and machine vision, Cengage Learning, 2014.
DOI : 10.1007/978-1-4899-3216-7

A. Bali and S. N. Singh, A Review on the Strategies and Techniques of Image Segmentation, 2015 Fifth International Conference on Advanced Computing & Communication Technologies, pp.113-120, 2015.
DOI : 10.1109/ACCT.2015.63

G. Mclachlan and D. Peel, Finite mixture models, 2004.
DOI : 10.1002/0471721182

C. M. Bishop, Pattern recognition and machine Learning, 2006.

N. Bouguila, Count Data Modeling and Classification Using Finite Mixtures of Distributions, IEEE Transactions on Neural Networks, vol.22, issue.2, pp.186-198, 2011.
DOI : 10.1109/TNN.2010.2091428

W. Fan, N. Bouguila, and D. Ziou, Variational learning for finite Dirichlet mixture models and applications, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.5, pp.762-774, 2012.

H. Wei, X. Wang, and L. L. Lai, Compact Image Representation Model Based on Both nCRF and Reverse Control Mechanisms, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.1, pp.150-162, 2012.
DOI : 10.1109/TNNLS.2011.2178472

S. E. Yuksel, J. N. Wilson, and P. D. Gader, Twenty Years of Mixture of Experts, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.8, pp.1177-1193, 2012.
DOI : 10.1109/TNNLS.2012.2200299

R. Guillemaud and M. Brady, Estimating the bias field of MR images, IEEE Transactions on Medical Imaging, vol.16, issue.3, pp.238-251, 1997.
DOI : 10.1109/42.585758

I. Wells, W. M. , W. Grimson, R. Kikinis, and F. Jolesz, Adaptive segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.15, issue.4, pp.429-442, 1996.
DOI : 10.1109/42.511747
URL : ftp://ftp.ai.mit.edu/pub/users/sw/papers/tmi-96.ps.gz

T. M. Nguyen and Q. Wu, Gaussian-Mixture-Model-Based Spatial Neighborhood Relationships for Pixel Labeling Problem, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.1, pp.193-202, 2012.
DOI : 10.1109/TSMCB.2011.2161284

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (methodological), vol.39, pp.1-38, 1977.

J. A. Bilmes, A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models, International Computer Science Institute, 1998.

T. Denoeux, Maximum likelihood estimation from fuzzy data using the EM algorithm Fuzzy sets and systems, pp.72-91, 2011.

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, 2007.

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.381-396, 2002.
DOI : 10.1109/34.990138
URL : http://www.cse.msu.edu/publications/tech/TR/MSU-CSE-00-21.ps.gz

P. Wu, Y. Liu, Y. Li, and B. Liu, Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images, IEEE Transactions on Medical Imaging, vol.34, issue.6, pp.1321-1335, 2015.
DOI : 10.1109/TMI.2015.2388699

R. Wagner, S. Smith, H. Sandrik, and . Lopez, Statistics of Speckle in Ultrasound B-Scans, IEEE Transactions on Sonics and Ultrasonics, vol.30, issue.3, pp.156-163, 1983.
DOI : 10.1109/T-SU.1983.31404

E. Jakeman, Speckle Statistics With A Small Number Of Scatterers, Optical Engineering, vol.23, issue.4, pp.234-453, 1984.
DOI : 10.1117/12.7973317

M. F. Insana, R. F. Wagner, D. G. Brown, and T. J. Hall, Describing small???scale structure in random media using pulse???echo ultrasound, The Journal of the Acoustical Society of America, vol.87, issue.1, pp.179-192, 1990.
DOI : 10.1121/1.399283

V. Dutt and J. F. Greenleaf, Distribution Signal Model, Ultrasonic Imaging, vol.14, issue.4, pp.265-287, 1994.
DOI : 10.1002/j.1538-7305.1945.tb00453.x

R. W. Prager, A. H. Gee, G. M. Treece, and L. H. Berman, Decompression and speckle detection for ultrasound images using the homodyned k-distribution, Pattern Recognition Letters, vol.24, issue.4-5, pp.705-713, 2003.
DOI : 10.1016/S0167-8655(02)00176-9
URL : http://svr-www.eng.cam.ac.uk/reports/svr-ftp/prager_tr397.ps.gz

P. M. Shankar, Ultrasonic tissue characterization using a generalized Nakagami model, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.48, issue.6, pp.1716-1720, 2001.
DOI : 10.1109/58.971725

V. Damerjian, O. Tankyevych, N. Souag, and E. Petit, Speckle characterization methods in ultrasound images???????A review, IRBM, vol.35, issue.4, pp.202-213, 2014.
DOI : 10.1016/j.irbm.2014.05.003

J. C. Seabra, F. Ciompi, O. Pujol, J. Mauri, P. Radeva et al., Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound, IEEE Transactions on Biomedical Engineering, vol.58, issue.5, pp.1314-1324, 2011.
DOI : 10.1109/TBME.2011.2106498

M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model, IEEE Transactions on Medical Imaging, vol.31, issue.8, pp.1509-1520, 2012.
DOI : 10.1109/TMI.2012.2190617

Y. Zhang, M. Brady, and S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.45-57, 2001.
DOI : 10.1109/42.906424

S. P. Chatzis and T. A. Varvarigou, A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation, IEEE Transactions on Fuzzy Systems, vol.16, issue.5, pp.1351-1361, 2008.
DOI : 10.1109/TFUZZ.2008.2005008

H. Tang, J. Dillenseger, X. D. Bao, and L. M. Luo, A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model, Computerized Medical Imaging and Graphics, vol.33, issue.8, pp.644-650, 2009.
DOI : 10.1016/j.compmedimag.2009.07.001
URL : https://hal.archives-ouvertes.fr/inserm-00411983

H. Zhang, Q. J. Wu, and T. M. Nguyen, Incorporating Mean Template Into Finite Mixture Model for Image Segmentation, IEEE Transactions on Neural Networks and Learning Systems, vol.24, issue.2, pp.328-335, 2013.
DOI : 10.1109/TNNLS.2012.2228227

J. Dillenseger, S. Laguitton, and E. Delabrousse, Fast simulation of ultrasound images from a CT volume, Computers in Biology and Medicine, vol.39, issue.2, pp.180-186, 2009.
DOI : 10.1016/j.compbiomed.2008.12.009
URL : https://hal.archives-ouvertes.fr/inserm-00130948

L. R. Dice, Measures of the Amount of Ecologic Association Between Species, Ecology, vol.26, issue.3, pp.297-302, 1945.
DOI : 10.2307/1932409

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314
URL : http://www.cis.rit.edu/~cnspci/references/dip/segmentation/haralick1973.pdf

C. Li, C. Xu, C. Gui, and M. D. 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.

J. A. Noble and D. Boukerroui, Ultrasound image segmentation: a survey, IEEE Transactions on Medical Imaging, vol.25, issue.8, pp.987-1010, 2006.
DOI : 10.1109/TMI.2006.877092
URL : https://hal.archives-ouvertes.fr/hal-00338658