, Taille de voxel de 150 microns. (e) Incisive 21. Taille de voxel de 300 microns. (f) Canine 23. Taille de voxel de 300 microns

, Taille de voxel de 300 microns. (i) Incisive 32. Taille de voxel de 300 microns. (j) Canine 33. Taille de voxel de 300 microns

, Molaire 37. Taille de voxel de 300 microns

, (m) Incisive 41. Taille de voxel de 150 microns. (n) Canine 43. Taille de voxel de 150 microns

, Molaire 47. Taille de voxel de 150 microns

B. Figure, 1 -Résultats obtenus par dent individuelle avec la méthode présentée dans le chapitre

, Pour chaque série est présentée la coupe axiale puis la coupe coronale et enfin la coupe sagittale, chacune avec leur résultat

, Coupe intermédiaire des dents maxillaires

, Coupe intermédiaire des dents maxillaires

, Rendu de profil gauche

, Rendu de profil droit

, Segmentation multi-objets (a) Incisive 11. Taille de voxel de 150 microns

, Taille de voxel de 150 microns. (c) Canine 33. Taille de voxel de 150 microns

, (d) Molaire 36. Taille de voxel de 150 microns

, Ci-après est illustrée notre ontologie sous sa forme graphique

, Vue globale du modèle avec les principales structures de la zone maxillo-faciale

, Ce concept est spécialisé selon le type de dent, ce qui lui confère des attributs (de forme notamment

, Les arches dentaires sont un groupement de dents aligné localement. Ici l'arche maxillaire. Notons que chaque dent est individualisée par son numéro dentaire

, Les arches dentaires sont un groupement de dents aligné localement. Ici l'arche mandibulaire. Notons que chaque dent est individualisée par son numéro dentaire

, Concept de l'ensemble uni des os maxillaires

, Concept d'une mandibule

D. Adalsteinsson and J. A. Sethian, A Fast Level Set Method for Propagating Interfaces, Journal of Computational Physics, vol.118, pp.269-277, 1995.

Y. Amit and D. Geman, Randomized Inquiries about Shape; an Application to Handwritten Digit Recognition, p.18, 1994.

J. Atif, C. Hudelot, G. Fouquier, I. Bloch, and E. Angelini, From Generic Knowledge to Specific Reasoning for Medical Image Interpretation Using Graph based Representations, International Joint Conference on Artificial Intelligence -IJCAI, pp.224-229, 2007.

J. Atif, C. Hudelot, O. Nempont, N. Richard, B. Batrancourt et al., GRA-FIP: A Framework for the Representation of Healthy and Pathological Cerebral Information

, IEEE International Symposium on Biomedical Imaging -ISBI, vol.87, p.22, 2007.

J. Atif, O. Nempont, O. Colliot, E. Angelini, and I. Bloch, Level set Deformable Models Constrained by Fuzzy Spatial Relations, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems -IPMU, vol.86, pp.236-241, 2006.

I. Banerjee, G. Patané, and M. Spagnuolo, Combination of Visual and Symbolic Knowledge: A Survey in Anatomy, Computers in Biology and Medicine, vol.80, pp.148-157, 2017.

C. Berger, T. Géraud, R. Levillain, N. Widynski, A. Baillard et al., Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging

, IEEE International Conference on Image Processing -ICIP, vol.4, p.109, 2007.

J. Besag, Statistical Analysis of Dirty Pictures, Journal of the Royal Statistical Society, vol.48, p.40, 1986.

S. Beucher and F. Meyer, The Morphological Approach to Segmentation: the Watershed Transformation, Mathematical Morphology in Image Processing, p.13, 1993.

I. Biederman, Recognition-by-components: a Theory of Human Image Understanding, Psychological Review, vol.94, p.22, 1987.

A. M. Bjorndal, W. G. Henderson, A. E. Skidmore, and F. H. Kellner, Anatomic Measurements of Human Teeth Extracted from Males between the ages of 17 and 21 years". Oral Surgery, Oral Medicine, Oral Pathology, vol.38, p.33, 1974.

R. Blanc, M. Reyes, C. Seiler, and G. Székely, Conditional Variability of Statistical Shape Models based on Surrogate Variables, Medical Image Computing and Computer-Assisted Intervention -MICCAI, vol.5762, pp.84-91, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00813743

R. Blanc, C. Seiler, G. Székely, L. P. Nolte, and M. Reyes, Statistical Model based Shape Prediction from a Combination of Direct Observations and Various Surrogates: Application to Orthopaedic Research, Medical Image Analysis, vol.16, issue.6, p.105, 2012.

I. Bloch, Fuzzy Relative Position between Objects in Image Processing: a Morphological Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, pp.657-664, 1999.

I. Bloch, Fuzzy Spatial Relationships for Image Processing and Interpretation: a Review, Image and Vision Computing, vol.23, pp.89-110, 2005.

I. Bloch, Fuzzy Spatial Relationships from Mathematical Morphology for Model-Based Pattern Recognition and Spatial Reasoning, International Conference on Discrete Geometry for Computer Imagery -DGCI, vol.2886, p.22, 2003.

I. Bloch, O. Colliot, O. Camara, and T. Géraud, Fusion of Spatial Relationships for Guiding Recognition, Example of Brain Structure Recognition in 3D MRI, Pattern Recognition Letters, vol.26, pp.449-457, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01251245

I. Bloch, O. Colliot, and R. M. Cesar, On the Ternary Spatial Relation "Between, IEEE Transactions on Systems, Man, and Cybernetics, vol.36, issue.2, pp.312-339, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01251251

I. Bloch and A. Ralescu, Directional Relative Position between Objects in Image Processing: a Comparison between Fuzzy Approaches, Pattern Recognition, vol.36, issue.7, pp.1563-1582, 2003.

M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel et al., VisualBackProp: Efficient Visualization of CNNs, p.23, 2016.

Y. Boykov and G. Funka-lea, Graph Cuts and Efficient N-D Image Segmentation, International Journal of Computer Vision, vol.70, issue.2, p.41, 2006.
DOI : 10.1007/s11263-006-7934-5

Y. Boykov and M. Jolly, Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images, IEEE International Conference on Computer Vision -ICCV, vol.37, p.10, 2001.

Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, pp.1124-1137, 2004.

Y. Boykov, O. Veksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, pp.67-69, 2001.

L. Brahim, K. Okba, and L. Robert, Mathematical Framework for Yopological Relationships between Ribbons and Regions, Journal of Visual Languages and Computing, vol.26, p.22, 2015.

L. Breiman, Random forests, Machine Learning, vol.45, p.18, 2001.

B. Caldairou, Arbre des Composantes Connexes : Méthodologie et Application à la Segmentation d'Images Médicales, p.13, 2008.

E. Carlinet and T. Géraud, A Comparison of Many Max-Tree Computation Algorithms, International Symposium on Mathematical Morphology -ISMM, vol.7883, p.109, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01476238

E. Carlinet and T. Géraud, MToS: A Tree of Shapes for Multivariate Images, IEEE Transactions on Image Processing, vol.24, p.13, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01474835

V. Caselles, R. Kimmel, and G. Sapiro, Geodesic Active Contours, International Journal of Computer Vision, vol.22, pp.9-10, 1997.
DOI : 10.1109/iccv.1995.466871

T. F. Chan and L. A. Vese, Active Contours Without Edges, IEEE Transactions on Image Processing, vol.10, pp.9-10, 2001.
DOI : 10.1109/83.902291

URL : http://www.math.ucla.edu/~lvese/PAPERS/IEEEIP2001.pdf

X. Chen, J. K. Udupa, A. Alavi, and D. A. Torigian, GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation, Computer Vision and Image Understanding, vol.117, p.17, 2013.

P. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg et al., Deep Reinforcement Learning from Human Preferences, p.23, 2017.

E. Clementini and P. Di-felice, Qualitative Representation of Positional Information, Artificial Intelligence, vol.95, p.22, 1997.
DOI : 10.1016/s0004-3702(97)00046-5

URL : https://doi.org/10.1016/s0004-3702(97)00046-5

O. Colliot, Représentation, Évaluation et Utilisation de Relations Spatiales pour l'Interprétation d'Images. Application à la Reconnaissance de Structures Anatomiques en Imagerie Médicale, p.22, 2003.

O. Colliot, O. Camara, and I. Bloch, Integration of Fuzzy Spatial Relations in Deformable Models-Application to Brain MRI Segmentation, Pattern Recognition, vol.39, issue.8, p.11, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00878443

R. W. Conners, M. M. Trivedi, and C. A. Harlow, Segmentation of a High-Resolution Urban Scene Using Texture Operators, Computer Vision, Graphics, and Image Processing, vol.25, p.132, 1984.

T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active Appearance Models, European Conference on Computer Vision -ECCV, vol.1407, p.28, 1998.

T. F. Cootes, C. J. Taylor, D. Cooper, and J. Graham, Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, p.17, 1995.

C. Couprie, L. Grady, L. Najman, and H. Talbot, Power Watershed: A Unifying Graph-based Optimization Framework, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, pp.54-55, 2011.

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, p.18, 2013.

A. Criminisi, J. Shotton, D. Robertson, and E. Konukoglu, Regression Forests for Efficient Anatomy Detection and Localization in CT Studies". Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, pp.106-117, 2011.

W. De-vos, J. Casselman, and G. R. Swennen, Cone-Beam Computerized Tomography (CBCT) Imaging of the Oral and Maxillofacial Region: A Systematic Review of the Literature, International Journal of Oral and Maxillofacial Surgery, vol.38, issue.6, pp.609-625, 2009.

T. Deschamps and L. D. Cohen, Fast Extraction of Tubular and Tree 3D Surfaces with Front Propagation Methods, International Conference on Pattern Recognition -ICPR, vol.1, p.60, 2002.

A. Desolneux, L. Moisan, and J. Morel, From Gestalt Theory to Image Analysis, Interdisciplinary Applied Mathematics, vol.34, p.22, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00259077

Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang et al., 3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images, Medical Image Analysis, vol.41, p.20, 2017.

G. J. Edwards, C. J. Taylor, and T. F. Cootes, Learning to Identify and Track Faces in Image Sequences, IEEE International Conference on Automatic Face and Gesture Recognition -ICAFGR, p.28, 1998.

T. Evain, X. Ripoche, J. Atif, and I. Bloch, Fuzzy "Along" Spatial Relation in 3D. Application to Anatomical Structures in Maxillofacial CBCT, International Conference on Image Analysis and Processing -ICIAP. Vol. LNCS 9279, pp.271-281, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01464716

T. Evain, X. Ripoche, J. Atif, and I. Bloch, Semi-automatic Teeth Segmentation in ConeBeam Computed Tomography by Graph-Cut with Statistical Shape Priors, IEEE International Symposium on Biomedical Imaging -ISBI, vol.2, pp.1197-1200, 2017.

J. Faure, A. Oueiss, J. Treil, S. Chen, V. Wong et al., Céphalométrie 3D et Intelligence Artificielle, vol.50, p.98, 2016.

L. R. Ford and D. R. Fulkerson, Maximal Flow Through a Network, Canadian Journal of Mathematics, vol.8, pp.399-404, 1956.

G. Fouquier, Optimisation de Séquences de Segmentation combinant Modèle Structurel et Focalisation de l'Attention Visuelle. Application à la Reconnaissance de Structures, vol.87, 2010.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, Multiscale Vessel Enhancement Filtering, International Conference on Medical Image Computing and Computer-Assisted Intervention -MICCAI, vol.1496, p.60, 1998.

D. Freedman and T. Zhang, Interactive Graph Cut based Segmentation with Shape Priors

, IEEE Conference on Computer Vision and Pattern Recognition -CVPR, vol.1, pp.17-18, 2005.

J. Freeman, The Modelling of Spatial Relations, Computer Graphics and Image Processing, vol.4, p.22, 1975.

M. Gahegan, Proximity Operators for Qualitative Spatial Reasoning, Spatial Information Theory : a theoretical basis for GIS, vol.988, p.22, 1995.

Y. Gan, Z. Xia, J. Xiong, Q. Zhao, Y. Hu et al., Toward Accurate Tooth Segmentation from Computed Tomography Images using a Hybrid Level set Model, Medical physics, vol.42, p.11, 2015.

H. Gao and O. Chae, Individual Tooth Segmentation from CT Images using Level set Method with Shape and Intensity Prior, Pattern Recognition, vol.43, pp.10-11, 2010.

M. Gargouri, J. Tierny, E. Jolivet, P. Petit, and E. D. Angelini, Accurate and Robust Shape Descriptors for the Identification of Rib Cage Structures in CT-Images with Random Forests
URL : https://hal.archives-ouvertes.fr/hal-01206863

, IEEE International Symposium on Biomedical Imaging -ISBI, vol.18, pp.54-55, 2013.

R. Gauriau, Méthodes Multi-Organes rapides avec A Priori de Forme pour la Localisation et la Segmentation en Imagerie Médicale 3D, p.18, 2015.

S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, p.40, 1984.

T. Géraud, E. Carlinet, S. Crozet, and L. Najman, A Quasi-linear Algorithm to Compute the Tree of Shapes of nD Images, International Symposium on Mathematical Morphology -ISMM

, LNCS, vol.7883, p.13, 2013.

P. Geurts, D. Ernst, and L. Wehenkel, Extremely Randomized Trees, Machine Learning, vol.63, issue.1, p.18, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00341932

B. Glocker, O. Pauly, E. Konukoglu, and A. Criminisi, Joint Classification-Regression Forests for Spatially Structured Multi-object Segmentation, European Conference on Computer Vision -ECCV, vol.7575, pp.870-881

E. S. Gopi and P. Palanisamy, Fast Computation of PCA Bases of Image Subspace using its Inner-product Subspace, Applied Mathematics and Computation, vol.219, p.29, 2013.

L. Grady, Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, p.10, 2006.

N. M. Grande, G. Plotino, R. Pecci, R. Bedini, C. H. Pameijer et al., Microcomputerized Tomographic Analysis of Radicular and Canal Morphology of Premolars with Long Oval Canals". Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology and Endodontology, vol.106, p.60, 2008.

D. M. Greig, B. T. Porteous, and A. H. Seheult, Exact Maximum A Posteriori Estimation for Binary Images, Journal of the Royal Statistical Society, vol.51, pp.271-279, 1989.

D. Grosgeorge, C. Petitjean, J. N. Dacher, and S. Ruan, Graph Cut Segmentation with a Statistical Shape Model in Cardiac MRI, Computer Vision and Image Understanding, vol.117, pp.1027-1035, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00936757

R. M. Haralick, Statistical and Structural Approaches To Texture, Proceedings of the IEEE, vol.67, p.132, 1979.

R. M. Haralick, K. Shanmugan, and I. Dinstein, Textural Features for Image Classification

, IEEE Transactions on Systems, Man, and Cybernetics. 3, vol.6, p.132, 1973.

W. G. Hayward and M. J. Tarr, Spatial Language and Spatial Representation, Cognition, vol.55, p.22, 1995.

L. T. Hiew, S. H. Ong, and K. W. Foong, Tooth Segmentation From Cone-Beam CT Using Graph Cut". Asia-Pacific Signal and Information Processing Association -APSIPA, p.11, 2010.

T. K. Ho, Random Decision Forests, International Conference on Document Analysis and Recognition -ICDAR, p.18, 1995.

C. Hudelot, J. Atif, and I. Bloch, Fuzzy Spatial Relation Ontology for Image Interpretation". Fuzzy Sets and Systems 159, vol.15, pp.1929-1951, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00824590

J. E. Iglesias, E. Konukoglu, A. Montillo, Z. Tu, and A. Criminisi, Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning, NeuroImage, vol.108, pp.25-36, 2011.

D. Kainmueller, H. Lamecker, H. Seim, M. Zinser, and S. Zachow, Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data, International Conference on Medical Image Computing and Computer-Assisted Intervention -MICCAI, vol.5762, p.105, 2009.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane et al., Efficient Multi-scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation, Medical Image Analysis, vol.36, pp.19-20, 2017.

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active Contour Models, International Journal of Computer Vision, vol.1, pp.321-331, 1988.

O. Koenig, L. P. Reiss, and S. M. Kosslyn, The Development of Spatial Relation Representations: Evidence from Studies of Cerebral Lateralization, Journal of Experimental Child Psychology, vol.50, p.22, 1990.

V. Kolmogorov and R. Zabih, What Energy Functions can be Minimized via Graph Cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, pp.67-68, 2004.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks". Conference on Neural Information Processing Systems -NIPS, vol.25, p.19, 2012.

F. L. Ber, J. Lieber, and A. Napoli, Encyclopédie de l'informatique et des systèmes d'information, pp.1197-1208, 2006.

J. L. Leopold, C. L. Sabharwal, K. J. Ward-;-topology, and M. , Spatial Relations between 3D Objects: The Association between Natural Language, Journal of Visual Languages and Computing, vol.27, p.22, 2015.

M. E. Leventon, W. E. Grimson, and O. Faugeras, Statistical Shape Influence in Geodesic Active Contours, IEEE Conference on Computer Vision and Pattern Recognition -CVPR

C. Li, C. Xu, C. Gui, and M. Fox, Level Set Evolution without Re-Initialization: A New Variational Formulation, IEEE Conference on Computer Vision and Pattern Recognition -CVPR, vol.1, p.10, 2005.

M. Li, A. Shekhovtsov, and D. Huber, Complexity of Discrete Energy Minimization Problems

, European Conference on Computer Vision -ECCV. Vol. LNCS 9905, p.40, 2016.

S. Livens, P. Scheunders, G. Van-de-wouwer, and D. Van-dyck, Wavelets for Texture Analysis, International Journal of Computer Science and Information Management, p.135, 1997.

J. Long, E. Shelhamer, and T. Darrell, Fully Convolutional Networks for Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition -CVPR, p.19, 2015.

D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, PET-CT Image Registration in the Chest using Free-form Deformations, IEEE Transactions on Medical Imaging, vol.22, p.29, 2003.

Y. Miki, C. Muramatsu, T. Hayashi, X. Zhou, T. Hara et al., Classification of Teeth in Cone-Beam CT using Deep Convolutional Neural Network, Computers in Biology and Medicine, vol.80, p.23, 2017.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level Control through Deep Reinforcement Learning, Nature, vol.518, p.23, 2015.

R. S. Montero and E. Bribiesca, State of the Art of Compactness and Circularity Measures, International Mathematical Forum, vol.4, p.76, 2009.

A. Montillo, J. Shotton, J. Winn, J. E. Iglesias, D. Metaxas et al., Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, International Conference on Information Processing in Medical Imaging -IPMI, vol.6801, pp.184-196, 2011.

J. N. Morgan and J. A. Sonquist, Problems in the Analysis of Survey Data, and a Proposal, Journal of the American Statistical Association, vol.58, p.18, 1963.

B. Naegel, N. Passat, N. Boch, and M. Kocher, Segmentation using Vector-Attribute Filters : Methodology and Application to Dermatological Imaging, International Symposium on Mathematical Morphology -ISMM, p.13, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00187232

L. Najman and M. Couprie, Building the Component Tree in Quasi-linear Time, IEEE Transactions on Image Processing, vol.15, issue.11, pp.3531-3539, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00622110

O. Nempont, Modèles Structurels Flous et Propagation de Contraintes pour la Segmentation et la Reconnaissance d'Objets dans les Images, vol.87, 2009.

H. Noh, S. Hong, and B. Han, Learning Deconvolution Network for Semantic Segmentation, IEEE International Conference on Computer Vision -ICCV, p.19, 2015.

S. Osher and J. A. Sethian, Fronts Propagating with Curvature-dependent Speed: Algorithms based on Hamilton-Jacobi Formulations, Journal of Computational Physics, vol.79, pp.12-49, 1988.

N. Otsu, A Threshold Selection Method from Gray-level Histograms, Automatica, vol.20, p.35, 1975.

Y. Pei, X. Ai, H. Zha, T. Xu, and G. Ma, 3D Exemplar-based Random Walks for Tooth Segmentation from Cone-Beam Computed Tomography Images, Medical Physics, vol.43, pp.15-16, 2016.

O. A. Penatti, K. Nogueira, and J. A. Santos, Do Deep Features generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains, IEEE Conference on Computer Vision and Pattern Recognition -CVPR, p.20, 2015.

P. Proff, Malocclusion, Mastication and the Gastrointestinal System, Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie, vol.71, issue.2, pp.96-107, 2010.

A. Rosenfeld, The Fuzzy Geometry of Image Subsets, Pattern Recognition Letters, vol.2, p.22, 1984.

F. Saint-pierre, G. Fanelli, L. Mosnegutu, and F. Devaux, Tomographie Volumique à Faisceau Conique de la Face (Cone Beam). Rapport technique. Haute Autorité de Santé, pp.1-10, 2009.

P. Salembier, A. Oliveras, and L. Garrido, Antiextensive Connected Operators for Image and Sequence Processing, IEEE Transactions on Image Processing, vol.7, pp.555-570, 1998.

W. C. Scarfe and A. G. Farman, What is Cone-Beam CT and How Does it Work?, Dental Clinics of North America, vol.52, pp.707-730, 2008.

N. Sebe and M. S. Lew, Wavelet Based Texture Classification, International Conference on Pattern Recognition -ICPR, vol.3, p.135, 2000.

M. Sepehrian, A. M. Deylami, and R. A. Zoroofi, Individual Teeth Segmentation in CBCT and MSCT Dental Images using Watershed, Iranian Conference on Biomedical Engineering -ICBME, pp.14-15, 2013.

J. A. Sethian, Fast Marching Methods and Level Set Methods for Propagating Interfaces, Computational Fluid Mechanics from von Karman Institute Lecture Series. von Karman Institute, p.9, 1998.

A. Shariff, M. J. Egenhofer, and D. M. Mark, Natural-Language Spatial Relations Between Linear and Areal Objects : The Topology and Metric of English, International Journal of Geographical Information Science, vol.12, p.22, 1998.

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations -ICRL, pp.19-20, 2015.

A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, p.22, 2000.

W. Sweldens, The Lifting Scheme: A Construction of Second Generation Wavelets, SIAM Journal on Mathematical Analysis, vol.29, p.135, 1998.

A. A. Taha and A. Hanbury, Metrics for Evaluating 3D Medical Image Segmentation: Analysis, Selection, and Tool, BMC Medical Imaging, vol.15, p.45, 2015.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall et al., Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, IEEE Transactions on Medical Imaging, vol.35, p.20, 2016.

C. M. Takemura, R. M. Cesar, and I. Bloch, Modeling and Measuring the Spatial Relation "Along": Regions, Contours and Fuzzy Sets, Pattern Recognition, vol.45, pp.75-79, 2012.

H. Urien, I. Buvat, N. Rougon, M. Soussan, and I. Bloch, Brain Lesion Detection in 3D PET Images Using Max-Trees and a New Spatial Context Criterion, International Symposium on Mathematical Morphology -ISMM, vol.10225, p.13, 2017.

M. C. Vanegas, Relations Spatiales et Raisonnement Spatial pour l'Interprétation des Images d'Observation de la Terre utilisant un Modèle Structurel, vol.22, pp.84-85, 2011.

M. C. Vanegas, I. Bloch, and J. Inglada, A Fuzzy Definition of the Spatial Relation "Surround"-Application to Complex Shapes, Conference of the European Society for Fuzzy Logic and Technology -EUSFLAT, pp.844-851, 2011.

M. C. Vanegas, I. Bloch, and J. Inglada, Alignment and Parallelism for the Description of HighResolution Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, vol.51, pp.3542-3557, 2013.

L. Wang, Y. Gao, F. Shi, G. Li, K. Chen et al., Automated Segmentation of CBCT Image with Prior-Guided Sequential Random Forest". Medical Computer Vision: Algorithms for Big Data, vol.9601, p.23, 2016.

C. D. Wickens, J. Lee, Y. Liu, S. E. , and G. Becker, An Introduction to Human Factors Engineering, p.107, 2004.

Z. Xia, Y. Gan, L. Chang, J. Xiong, and Q. Zhao, Individual Tooth Segmentation from CT Images scanned with Contacts of Maxillary and Mandible Teeth, Computer Methods and Programs in Biomedicine, vol.138, p.11, 2017.

Y. Xu, Espaces des Formes basés sur des Arbres : Définition et Applications en Traitement d'Images et Vision par Ordinateur, p.13, 2013.

Y. Xu, T. Géraud, and I. Bloch, From Neonatal to Adult Brain MR Image Segmentation in a Few Seconds Using 3D-like Fully Convolutional Network and Transfer Learning, IEEE International Conference on Image Processing -ICIP, vol.56, p.20, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01735727

L. A. Zadeh, Fuzzy Logic = Computing with words, IEEE Transactions on Fuzzy Systems, vol.4, p.22, 1996.

L. A. Zadeh, Fuzzy Sets". Information and Control, vol.8, p.22, 1965.

Z. Zou, S. Luo, J. Pan, S. Liu, and S. Liao, A Semi-automatic Segmentation for Tooth on Cone Beam CT Volume Following the Anatomic Guidance, Journal of Information Hiding and Multimedia Signal Processing, vol.8, pp.12-13, 2017.

R. Zwick, E. Carlstein, and D. V. Budescu, Measures of Similarity Among Fuzzy Concepts: A Comparative Analysis, International Journal of Approximate Reasoning, vol.1, p.84, 1987.