D. Cremers, T. Kohlberger, and &. C. Schnörr, Shape statistics in kernel space for variational image segmentation, Pattern Recognition, vol.36, issue.9, pp.1929-1943, 2003.
DOI : 10.1016/S0031-3203(03)00056-6

D. Cremers, S. J. Osher, and &. S. Soatto, Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation, International Journal of Computer Vision, vol.127, issue.2, pp.335-351, 2006.
DOI : 10.1007/s11263-006-7533-5

D. Cremers, M. Rousson, and &. R. Deriche, A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape, International Journal of Computer Vision, vol.18, issue.9, pp.195-215, 2007.
DOI : 10.1007/s11263-006-8711-1

R. N. Czerwinski, D. L. Jones, and &. W. O-'brien-jr, Ultrasound speckle reduction by directional median filtering, Proceedings., International Conference on Image Processing, pp.358-361, 1995.
DOI : 10.1109/ICIP.1995.529720

E. Debreuve, M. Barlaud, G. Aubert, and &. J. Darcourt, Space time segmentation using level set active contours applied to myocardial gated SPECT
URL : https://hal.archives-ouvertes.fr/hal-00367622

M. Barlaud and &. G. Aubert, Using the shape gradient for active contour segmentation : from the continuous to the discrete formulation, Journal of Mathematical Imaging and Vision, vol.28, issue.21, pp.47-66, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00417707

M. C. Delfour and &. J. Zolésio, Shape and geometries, Advances in Design and Control, SIAM, p.20, 2001.

L. Demanet and &. L. Ying, Wave atoms and time upscaling of wave equations, Numerische Mathematik, vol.48, issue.1, p.90, 2008.
DOI : 10.1007/s00211-009-0226-6

L. J. Didio, &. H. Rodrigues, M. N. Do, and &. M. Vetterli, Cardiac segments in the human heart Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, Surgical and Radiologic Anatomy IEEE Transactions on Image Processing, vol.5, issue.35, pp.115-124, 1983.

V. Dutt, Statistical Analysis of Ultrasound Echo Enveloppe, pp.43-124, 1995.

V. Dutt, &. J. Greenleaf, M. Gastaud, M. Barlaud, and &. G. Aubert, Adaptive speckle reduction filter for log Tracking video objects using active contours and geometric priors, IEEE Workshop on Image Analysis for Multimedia Interactive Services, pp.170-175, 2003.

S. Geman and &. D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Readings in uncertain reasoning, pp.452-472, 1990.

]. B. Georgescu, X. S. Zhou, D. Comaniciu, and &. A. Gupta, Database-Guided Segmentation of Anatomical Structures with Complex Appearance, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.429-436, 2005.
DOI : 10.1109/CVPR.2005.119

F. Goudail and &. P. Réfrégier, Statistical image processing techniques for noisy images, p.47, 2004.
DOI : 10.1007/978-1-4419-8855-3

URL : https://hal.archives-ouvertes.fr/hal-00080004

L. Grady and &. Christopher-alvino, Reformulating and Optimizing the Mumford-Shah Functional on a Graph ??? A Faster, Lower Energy Solution, European Conference on Computer Vision, pp.248-261, 2008.
DOI : 10.1007/978-3-540-88682-2_20

H. Gudbjartsson and &. S. Patz, The rician distribution of noisy mri data, Magnetic Resonance in Medicine, vol.3, issue.6, pp.910-914, 1995.
DOI : 10.1002/mrm.1910340618

]. G. Hamarneh and &. T. Gustavsson, Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), pp.115-118, 2000.
DOI : 10.1109/CIC.2000.898469

A. K. Hamou, S. Osman, and &. R. , Carotid Ultrasound Segmentation Using DP Active Contours, International Conference on Image Analysis and Recognition, p.129, 2007.
DOI : 10.1007/978-3-540-74260-9_85

]. J. Hansegard-07a, F. Hansegard, &. S. Orderud, and . Rabben, Real-Time Active Shape Models for Segmentation of 3D Cardiac Ultrasound, Computer Analysis of Images and Patterns, pp.157-164, 2007.
DOI : 10.1007/978-3-540-74272-2_20

]. J. Hansegard-07b, S. Hansegard, K. Urheim, &. S. Lunde, and . Rabben, Constrained Active Appearance Models for Segmentation of Triplane Echocardiograms, IEEE Transactions on Medical Imaging, vol.26, issue.10
DOI : 10.1109/TMI.2007.900692

B. Hao, S. Gao, and &. X. Gao, A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing, IEEE Transactions on Medical Imaging, vol.18, issue.9, pp.787-794, 0124.

C. Hao, C. Bruce, &. J. Pislaru, and . Greenleaf, Segmenting high-frequency intracardiac ultrasound images of myocardium into infracted, ischemic and normal regions, IEEE Transactions on Medical Imaging, vol.20, issue.125, pp.1373-1383, 2001.

]. A. Herbulot-04a, S. Herbulot, M. Jehan-besson, &. G. Barlaud, and . Aubert, Shape gradient for image segmentation using information theory, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004.
DOI : 10.1109/ICASSP.2004.1326471

]. A. Herbulot-04b, S. Herbulot, M. Jehan-besson, &. G. Barlaud, and . Aubert, Shape gradient for multi-modal image segmentation using mutual information, 2004 International Conference on Image Processing, 2004. ICIP '04., 2004.
DOI : 10.1109/ICIP.2004.1421668

]. I. Herlin-93, &. G. Herlin, ]. A. Giraudon, &. J. Hill, and . Taylor, Performing Segmentation of Ultrasound Images Using Temporal Information Model-Based Image Interpretation Using Genetic Algorithms, Computer Vision and Pattern Recognition, pp.295-300, 1992.

]. A. Hill-93, A. Hill, &. C. Thornham, and . Taylor, Model-Based Interpretation of 3D Medical Images, Procedings of the British Machine Vision Conference 1993, p.125, 1993.
DOI : 10.5244/C.7.34

P. S. Hiransakolwong, K. A. Windyga, &. K. Hua, and . Vu, FASU: a Full Automatic Segmenting System for Ultrasound images, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings., p.129, 2002.
DOI : 10.1109/ACV.2002.1182163

]. S. Jehan-besson-03a and . Jehan-besson, Modèles de contours actifs basés régions pour la segmentation d'images et de vidéos, pp.33-105, 2003.

]. S. Jehan-besson-03b, M. Jehan-besson, &. G. Barlaud, and . Aubert, DREAM 2 S : Deformable Regions driven by an Eulerian Accurate Minimization Method for image and video segmentation, International Journal of Computer Vision, issue.53, pp.45-70, 2003.

]. S. Jehan-besson-03c, M. Jehan-besson, &. G. Barlaud, and . Aubert, Shape gradients for histogram segmentation using active contours, Proceedings Ninth IEEE International Conference on Computer Vision, p.31, 2003.
DOI : 10.1109/ICCV.2003.1238375

Z. J. Jiang, F. Zhang, H. T. Cen, &. K. Tsui, and . Lau, An enhanced appearance model for ultrasound image segmentation, International Conference on Pattern Recognition, p.129, 2004.

]. B. Julesz, Visual Pattern Discrimination, IEEE Transactions on Information Theory, vol.8, issue.2, pp.84-92, 1962.
DOI : 10.1109/TIT.1962.1057698

T. Kailath, The Divergence and Bhattacharyya Distance Measures in Signal Selection, IEEE Transactions on Communications, vol.15, issue.1, pp.52-60, 1967.
DOI : 10.1109/TCOM.1967.1089532

I. Karoui, R. Fablet, J. M. Boucher, and &. J. Augustin, Region-Based Image Segmentation Using Texture Statistics And Level-Set Methods, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.693-696, 2006.
DOI : 10.1109/ICASSP.2006.1660437

M. Kass, A. Witkin, and &. D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-332, 1988.
DOI : 10.1007/BF00133570

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318

]. K. Kim, S. H. Park, and &. J. Kim, Kernel Principal Component Analysis for Texture Classification, IEEE Signal Processing Letters, vol.8, issue.2, pp.29-41, 2001.

]. J. Kim-02a, J. Kim, I. Fisher, A. Y. Jr, M. Cetin et al., Nonparametric Methods for Image Segmentation Using Information Theory and Curve Evolution, International Conference on Image Processing, pp.31-32, 1929.

. Bibliographie, . I. Kim-02b-]-k, K. C. Kim, S. H. Jung, &. J. Park et al., Support Vector Machines for Texture Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.11, pp.1542-1550, 2002.

]. S. Kim and &. J. Kang, Texture classification and segmentation using wavelet packet frame and Gaussian mixture model, Pattern Recognition, vol.40, issue.4, pp.1207-1221, 2007.
DOI : 10.1016/j.patcog.2006.09.012

S. Kischenassamy, A. Kumar, P. Olver, A. Tannenbaum, and &. A. Yezzi, Conform curvature flows : From phase transitions to active vision Archive for Rational Mechanics and Analysis, Koopman 36] P. O. Koopman. On distributions admitting a sufficient statistic. Transactions of the, pp.275-301, 1936.

J. Lebossé, F. Lecellier, M. Revenu, and &. E. Saloux, Segmentation du contour de l'endocarde sur des séquences d'images d'échographie cardiaque, GRETSI, p.138, 2005.

M. Leventon, W. Eric, L. Grimson, and &. O. Faugeras, Statistical Shape Influence in Geodesic Active Contours, Computer Vision and Pattern Recognition, pp.1316-1323, 2000.

&. A. Obadia and . Gee, Adaptive segmentation of ultrasound images, Image and Vision Computing, vol.17, issue.8, pp.583-588, 1999.
DOI : 10.1016/S0262-8856(98)00177-2

]. L. Liang, C. Liu, Y. Xu, B. Guo, and &. H. Shum, Real-time texture synthesis by patch-based sampling, ACM Transactions on Graphics, vol.20, issue.3, p.35, 2001.
DOI : 10.1145/501786.501787

]. S. Liao and &. M. Pawlak, On image analysis by moments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.3, pp.254-266, 1996.
DOI : 10.1109/34.485554

W. Lin, &. J. Yu, and . Duncan, Combinative multi-scale level set framework for echocardiographic image segmentation, Medical Image Analysis, vol.7, issue.4, pp.529-537, 2003.
DOI : 10.1016/S1361-8415(03)00035-5

S. Malassiotis and &. M. Strintzis, Tracking the left ventricle in echocardiographic images by learning heart dynamics, IEEE Transactions on Medical Imaging, vol.18, issue.3, pp.282-290, 1999.
DOI : 10.1109/42.764905

R. Malladi, J. A. Sethian, and &. C. Vemuri, Shape modeling with front propagation: a level set approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.2, pp.158-175, 1995.
DOI : 10.1109/34.368173

S. G. Mallat, A wavelet tour of signal processing, p.76, 1998.

]. A. Mansouri and &. J. Konrad, Motion segmentation with level sets, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), p.15, 1999.
DOI : 10.1109/ICIP.1999.822868

P. Martin, P. Réfrégier, F. Goudail, and &. F. Guérault, Influence of the noise model on level set active contour segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.6, pp.799-803, 2004.
DOI : 10.1109/TPAMI.2004.11

URL : https://hal.archives-ouvertes.fr/hal-00079480

]. P. Martin-06, P. Martin, F. Réfrégier, &. F. Galland, and . Guerault, Nonparametric statistical snake based on the minimum stochastic complexity, IEEE Transactions on Image Processing, vol.15, issue.9, pp.2769-2770, 2006.
DOI : 10.1109/TIP.2006.877317

S. Menet, P. Marc, and &. G. Medioni, B-snakes : Implementation and application to stereo, Image Understanding Workshop, pp.720-726, 1990.

F. G. Meyer and &. R. Coifman, Brushlets: A Tool for Directional Image Analysis and Image Compression, Applied and Computational Harmonic Analysis, vol.4, issue.2, pp.147-187, 1997.
DOI : 10.1006/acha.1997.0208

O. Michailovich, Y. Rathi, and &. A. Tannenbaum, Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow, IEEE Transactions on Image Processing, vol.16, issue.11, pp.2787-2801, 2007.
DOI : 10.1109/TIP.2007.908073

I. Miki?, S. Krucunski, &. J. Thomas, P. K. Mishra, &. M. Dutta et al., Segmentation and tracking in echocardiographic sequences : active contours guided by optical flow estimates A GA based approach for boundary detection of left ventricle with echocardiographic image sequences, IEEE Transactions on medical imaging Image and Vision Computing, vol.17, issue.125, pp.272-284, 1998.

. Bibliographie, Integrated active contours for texture segmentation, IEEE Transactions on image processing, vol.1, pp.1-19, 2004.

C. Samson, L. Blanc-féraud, G. Aubert, and &. J. Zerubia, A Level Set Model for Image Classification, International Journal of Computer Vision, vol.40, issue.3, pp.187-197, 2000.
DOI : 10.1007/3-540-48236-9_27

G. Sapiro, Geometric partial differential equations and image analysis
DOI : 10.1017/CBO9780511626319

D. K. Savelonas, &. D. Iakovidis, and . Maroulis, LBP-guided active contours, Pattern Recognition Letters, vol.29, issue.9, p.36, 2008.
DOI : 10.1016/j.patrec.2008.02.013

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2491

D. W. Scott, Multivariate density estimation : Theory, practice, and visualization (wiley series in probability and statistics), p.31, 1992.

J. A. Sethian, Level set methods and fast marching methods : Evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials sciences, p.25, 1999.

B. W. Silverman, Density estimation for statistics and data analysis
DOI : 10.1007/978-1-4899-3324-9

G. Slabaugh, G. Unal, T. Fang, and &. M. Wels, Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.45-53, 2006.
DOI : 10.1109/CVPR.2006.318

URL : http://openaccess.city.ac.uk/6079/1/Ultrasound%20specific.pdf

J. Sokolowski and &. J. Zolésio, Introduction to shape optimization, volume 16 of Springer series in computational mathematics, p.32, 1992.

L. H. Staib and &. J. Duncan, Boundary finding with parametrically deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.11, pp.1061-1075, 1992.
DOI : 10.1109/34.166621

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.835

J. L. Starck, M. Elad, and &. D. Donoho, Redundant multiscale transforms and their application for morphological component analysis, AIEP, pp.132-167, 2004.

M. B. Stegmann, Active Appearance Models, p.129, 2000.

C. Sterken and &. J. Manfroid, Astronomical photometry, p.43, 1992.

G. D. Stetten and &. R. Drezek, ACTIVE FOURIER CONTOUR APPLIED TO REAL TIME 3D ULTRASOUND OF THE HEART, International Journal of Image and Graphics, vol.01, issue.04, pp.647-658, 2001.
DOI : 10.1142/S0219467801000347

S. M. Szilagyi, L. Szilagyi, &. Z. Benyo, Z. Tao, H. D. Tagare et al., Echocardiographic Image Sequence Compression Based on Spatial Active Appearance Model Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images, Iberoamerican Congress on Pattern Recognition, pp.841-850, 2006.

M. Taron, N. Paragios, and &. M. Jolly, From Uncertainties to Statistical Model Building and Segmentation of the Left Ventricle, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409129

M. Taron, N. Paragios, and &. M. Jolly, Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.1, pp.99-113, 2009.
DOI : 10.1109/TPAMI.2008.36

URL : https://hal.archives-ouvertes.fr/hal-00712244

T. Taxt, A. Lundervold, and &. B. Angelsen, Noise reduction and segmentation in time-varying ultrasound images, [1990] Proceedings. 10th International Conference on Pattern Recognition, pp.591-596, 1990.
DOI : 10.1109/ICPR.1990.118170

S. T. Tay, &. J. Acton, and . Hossack, Ultrasound Despeckling Using an Adaptive Window Stochastic Approach, 2006 International Conference on Image Processing, pp.2549-2552, 2006.
DOI : 10.1109/ICIP.2006.312979

M. R. Teague, Image analysis via the general theory of moments*, The & R. T. Chin. On image analysis by the methods of moments, pp.920-930, 1980.
DOI : 10.1364/JOSA.70.000920

A. Bibliographie, A. Tsai, &. W. Yezzi, and . Wells, A Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets, IEEE Transactions on Medical Imaging, vol.22, issue.37, pp.137-154, 2003.

]. L. Vese and &. T. Chan, A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, IEEE Transactions on Image Processing, pp.1549-1560, 1995.

]. R. Wagner, 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

]. W. Wickerhauser-94 and . Wickerhauser, Adapted wavelet analysis from theory to software

G. Winkler-huang, I. Martin, and &. D. Metaxas, Image analysis, random fields and dynamic monte carlo methods a mathe-matical introduction Patch-based texture edges and segmentation, European Conference of Computer Vision, pp.18-35, 1995.

S. C. Wu, &. X. Zhu, and . Liu, Equivalence of Julesz Ensembles and FRAME Models, International Journal of Computer Vision, vol.38, issue.3, pp.247-265, 2000.
DOI : 10.1023/A:1008199424771

J. Xie, Y. Jiang, and &. T. Tsui, Segmentation of Kidney from Ultrasound Images Based on Texture ans Shape Priors, IEEE Transactions on Medical Imaging, vol.24, issue.125, pp.45-57, 2005.

]. A. Yezzi, A. Tsai, and &. A. Willsky, A statistical approach to snakes for bimodal and trimodal imagery, Proceedings of the Seventh IEEE International Conference on Computer Vision, p.29, 1999.
DOI : 10.1109/ICCV.1999.790317

L. Ying and &. L. Demanet, Wave atoms and sparsity of oscillatory patterns

Y. , ]. Y. Yu, and &. S. Aston, Edge detextion in ultrasound imagery using the instantaneous coefficient of variation, IEEE Transactions on Image Processing, vol.13, pp.1640-1655, 2004.

]. T. Zhang and &. D. Freedman, Tracking objects using density matching and shape priors, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1056-1062, 2003.
DOI : 10.1109/ICCV.2003.1238466

S. Zhu, T. S. Lee, 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.416-423, 1995.
DOI : 10.1109/ICCV.1995.466909

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