B. Benmiloud and W. Pieczynski, Estimation des paramètres dans les chaînes de Markov cachées et segmentation d'images Traitement du signal, pp.433-454, 1995.

G. Celeux and J. Diebolt, algorithme SEM : un algorithme d'apprentissage probabiliste pour la reconnaissance de mélanges de densités Revue de statistique appliquée, 1986.

J. Chen, S. Gunn, and M. Nixon, Markov Random Field Models for Segmentation of PET Images, Lecture Notes on Computer. Science, vol.2082, pp.468-474, 2001.
DOI : 10.1007/3-540-45729-1_50

J. Dai, Hybrid approach to speech recognition using hidden Markov models and Markov chains, IEE Proceedings - Vision, Image, and Signal Processing, vol.141, issue.5, pp.273-279, 1994.
DOI : 10.1049/ip-vis:19941321

A. P. Dempster, N. M. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B, vol.39, pp.1-38, 1977.

Y. Delignon, A. Marzouki, and W. Pieczynski, Estimation of Generalized Mixtures and Its Application in, Image Segmentation IEEE Transactions on Image Processing, vol.6, issue.10, 1997.
URL : https://hal.archives-ouvertes.fr/hal-00681507

H. Jarritt, K. Carson, and A. Hounsel, The role of PET/CT scanning in radiotherapy planning, The British Journal of Radiology, vol.79, issue.special_issue_1, pp.27-35
DOI : 10.1259/bjr/35628509

K. Jordan, IEC emission phantom Appendix Performance evaluation of positron emission tomographs Medical and Public Health Research Programme of the European Community, 1990.

M. Hatt, C. Roux, and D. Visvikis, 3D FUZZY ADAPTIVE UNSUPERVISED BAYESIAN SEGMENTATION FOR VOLUME DETERMINATION IN PET, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007.
DOI : 10.1109/ISBI.2007.356855

J. Kim, D. Feng, T. Cai, and S. Eberl, Automatic 3D Temporal Kinetics Segmentation of Dynamic Emission Tomography Image Using Adaptative Region Growing Cluster Analysis IEEE, Conference Record, vol.3, pp.1580-1583, 2002.

N. Krak and R. Boellaard, Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial Eur, J. Nucl. Med. Mol. Im, 2005.

F. Lamare, A. Turzo, Y. Bizais, C. Le-rest, C. Visvikis et al., Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE, Physics in Medicine and Biology, vol.51, issue.4, pp.943-962, 2006.
DOI : 10.1088/0031-9155/51/4/013

P. Lanchantin and W. Pieczynski, Unsupervised non stationary image segmentation using triplet Markov chains Advanced Concepts for Intelligent Vision Systems, 2004.

J. Marroquin, S. Mitter, and T. Poggio, Probabilistic Solution of Ill-Posed Problems in Computational Vision, Journal of the American Statistical Association, vol.18, issue.397, pp.76-89, 1987.
DOI : 10.1080/01621459.1987.10478393

J. Mcqueen, Some methods for classification and analysis of multivariate observations Proceedings of the Fifth Berkeley Symposium on, Mathematical Statistics and Probability, vol.1, pp.281-297, 1967.

U. Nestle, S. Kremp, A. Schaefer-schuler, C. Sebastian-welch, D. Hellwig et al., Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer, Journal of Nuclear Medicine, vol.46, issue.8, pp.1342-1350, 2005.

W. Pieczynski, Modèles de Markov en traitement d'images Traitement du Signal, pp.255-277, 2003.

A. Reader, A. S. Bakatselos, F. Manavaki, R. Walledge, R. Jeavons et al., Regularized one-pass list-mode EM algorithm for high resolution 3D PET image reconstruction into large arrays, 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310), pp.693-699, 2002.
DOI : 10.1109/NSSMIC.2001.1009185

C. Riddell, P. Brigger, R. Carson, and S. Bacharach, The Watershed Algorithm: A Method to Segment Noisy PET Transmission, Images IEEE Transactions on Nuclear Science, vol.46, issue.3, pp.731-719, 1999.

F. Salzenstein and W. Pieczynski, Parameter Estimation in hidden fuzzy Markov random fields and image segmentation, CVGIP : Graphical Models and Image Processing, pp.205-220, 1997.

F. Salzenstein and W. Pieczynski, Sur le choix de méthode de segmentation statistique d'images Traitement du signal, pp.119-128, 1998.

F. Salzenstein, C. Collet, and M. Petremand, Champs de Markov flous pour images multispectrales, Traitement du signal, vol.21, issue.1, pp.37-54, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00134462

D. Visvikis, C. Le-rest, C. Costa, and D. , Influence of OSEM and segmented attenuation correction in the calculation of standardised uptake values for [18F]FDG PET, European Journal of Nuclear Medicine, vol.28, issue.9, pp.1326-1335, 2001.
DOI : 10.1007/s002590100566