R. Achanta, . Shaji, . Appu, . Smith, A. Lucchi et al., SLIC superpixels compared to state-of-the-art superpixel methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.11, pp.2274-2282, 2012.

L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The annals of mathematical statistics, pp.164-171, 1970.

S. Bricq, C. Collet, and J. Armspach, Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains, Medical Image Analysis, vol.12, issue.6, pp.639-652, 2008.
DOI : 10.1016/j.media.2008.03.001

G. Celeux and J. Diebolt, L'algorithme SEM: un algorithme d'apprentissage probabiliste: pour la reconnaissance de mélange de densités, pp.35-52, 1986.

R. O. Duda, P. E. Hart, . Stork, and G. David, Pattern classification, 2012.

R. Fjortoft, Y. Delignon, W. Pieczynski, M. Sigelle, and F. Tupin, Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. Geoscience and Remote Sensing, IEEE Transactions on, vol.41, issue.3, pp.675-686, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01347239

B. Glocker, J. Feulner, A. Criminisi, D. R. Haynor, and E. Konukoglu, Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans, Medical Image Computing and Computer-Assisted Intervention?MICCAI, 2012.
DOI : 10.1007/978-3-642-33454-2_73

Y. Kim and D. Kim, A fully automatic vertebra segmentation method using 3D deformable fences, Computerized Medical Imaging and Graphics, vol.33, issue.5, pp.343-352, 2009.
DOI : 10.1016/j.compmedimag.2009.02.006

T. Klinder, J. Ostermann, M. Ehm, A. Franz, R. Kneser et al., Automated model-based vertebra detection, identification, and segmentation in CT images, Medical Image Analysis, vol.13, issue.3, pp.471-482, 2009.
DOI : 10.1016/j.media.2009.02.004

J. Ma, L. Lu, Y. Zhan, . Zhou, . Xiang et al., Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model, Medical Image Computing and Computer-Assisted Intervention?MICCAI, 2010.

H. Mirzaalian, M. Wels, T. Heimann, . Kelm, . Michael et al., Fast and robust 3D vertebra segmentation using statistical shape models, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013.
DOI : 10.1109/EMBC.2013.6610266

R. Nock and F. Nielsen, Statistical region merging. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, issue.11, pp.1452-1458, 2004.
DOI : 10.1109/tpami.2004.110
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.1930

A. Rasoulian, R. Rohling, and A. , Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model, IEEE Transactions on Medical Imaging, vol.32, issue.10, p.1890, 1900.
DOI : 10.1109/TMI.2013.2268424

N. Sharma, . Aggarwal, and M. Lalit, Automated medical image segmentation techniques Journal of medical physics/Association of Medical Physicists of India)3, 2010. [15] Sprawls, Perry. Physical principles of medical imaging, Medical Physics Pub, vol.35, issue.1, 1995.