J. Atif, H. Khotanlou, E. Angelini, H. Duffau, and I. Bloch, Segmentation of Internal Brain Structures in the Presence of a Tumor, Medical Image Computing and Computer-Assisted Intervention-Oncology Workshop (MICCAI), pp.61-68, 2006.

H. Khotanlou, J. Atif, E. Angelini, H. Duffau, and I. Bloch, ADAPTIVE SEGMENTATION OF INTERNAL BRAIN STRUCTURES IN PATHOLOGICAL MR IMAGES DEPENDING ON TUMOR TYPES, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.588-591, 2007.
DOI : 10.1109/ISBI.2007.356920

H. Khotanlou, J. Atif, B. Batrancourt, O. Colliot, E. Angelini et al., Segmentation de tumeurs cérébrales et intégration dans un modèle de l'anatomie, Reconnaissance des Formes et Intelligence Artificielle, RFIA'06, 2006.

H. Khotanlou, J. Atif, O. Colliot, and I. Bloch, 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models, WILF2005, volume 3849 of Lecture notes in computer science(LNCS), pp.312-318, 2005.
DOI : 10.1007/11676935_39

URL : https://hal.archives-ouvertes.fr/hal-01251278

H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, Brain tumor detection and segmentation using fuzzy classification, symmetry analysis and deformable model. Fuzzy Sets and Systems, 2007.

H. Khotanlou, O. Colliot, and I. Bloch, Automatic Brain Tumor Segmentation Using Symmetry Analysis and Deformable Models, Advances in Pattern Recognition, pp.198-202, 2007.
DOI : 10.1142/9789812772381_0032

M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.21, issue.3, pp.193-199, 2002.
DOI : 10.1109/42.996338

M. E. Algorri and F. Flores-mangas, Classification of Anatomical Structures in MR Brain Images Using Fuzzy Parameters, IEEE Transactions on Biomedical Engineering, vol.51, issue.9, pp.1599-1608, 2004.
DOI : 10.1109/TBME.2004.827532

C. Ambroise, M. Dang, and G. Govaert, Clustering of spatial data by the EM algorithm, chapter GEOENV I (Geostatistics for Environmental Applications), pp.493-504, 1995.

J. K. Anlauf and M. Biehl, The AdaTron: An Adaptive Perceptron Algorithm, Europhysics Letters (EPL), vol.10, issue.7, pp.687-692, 1989.
DOI : 10.1209/0295-5075/10/7/014

T. S. Armstrong, M. Z. Cohen, J. Weinbrg, and M. R. Gilbert, Imaging techniques in neuro-oncology, Seminars in Oncology Nursing, pp.231-239, 2004.
DOI : 10.1016/S0749-2081(04)00087-7

J. Atif, H. Khotanlou, E. Angelini, H. Duffau, and I. Bloch, Segmentation of Internal Brain Structures in the Presence of a Tumor, Medical Image Computing and Computer-Assisted Intervention-Oncology Workshop (MICCAI), pp.61-68, 2006.

J. Atif, O. Nempont, O. Colliot, E. Angelini, and I. Bloch, Level Set Deformable Models Constrained by Fuzzy Spatial Relations, Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU, pp.1534-1541, 2006.

L. Aurdal, Analyse d'images IRM 3D multi-´ echos pour la détection et la quantification de pathologies cérébrales, 1997.

M. Bach-cuadra, O. Cuisenaire, R. Meuli, and J. Thiran, Automatic segmentation of internal structures of the brain in MR images using a tandem of affine and non-rigid registration of an anatomical brain atlas, pp.1083-1086, 2001.

M. Bach-cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J. Villemure et al., Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Transactions on Medical Imaging, issue.10, pp.231301-1313, 2004.

C. Baillard, P. Hellier, and C. Barillot, Segmentation of brain 3D MR images using level sets and dense registration, Medical Image Analysis, vol.5, issue.3, pp.185-194, 2001.
DOI : 10.1016/S1361-8415(01)00039-1

URL : https://hal.archives-ouvertes.fr/inria-00536389

V. Barra and J. Boire, Automatic segmentation of subcortical brain structures in MR images using information fusion, IEEE Transactions on Medical Imaging, vol.20, issue.7, pp.549-558, 2001.
DOI : 10.1109/42.932740

H. Belitz, K. Rohr, H. Müller, and G. Wagenknecht, First Results of an Automated Model-Based Segmentation System for Subcortical Structures in Human Brain MRI Data, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.402-405, 2006.
DOI : 10.1109/ISBI.2006.1624938

J. Bezdek, R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, vol.10, issue.2-3, pp.191-203, 1984.
DOI : 10.1016/0098-3004(84)90020-7

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, 1981.
DOI : 10.1007/978-1-4757-0450-1

J. C. Bezdek, R. J. Hathaway, M. J. Sabin, T. , and W. T. , Convergence theory for fuzzy c-means: Counterexamples and repairs, IEEE Transactions on Systems, Man, and Cybernetics, vol.17, issue.5, pp.73-877, 1987.
DOI : 10.1109/TSMC.1987.6499296

I. Bloch, Fuzzy relative position between objects in image processing: a morphological approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.7, pp.657-664, 1999.
DOI : 10.1109/34.777378

I. Bloch, On fuzzy distances and their use in image processing under imprecision, Pattern Recognition, vol.32, issue.11, pp.1873-1895, 1999.
DOI : 10.1016/S0031-3203(99)00011-4

I. Bloch, Fuzzy spatial relationships for image processing and interpretation: a review, Image and Vision Computing, vol.23, issue.2, pp.89-110, 2005.
DOI : 10.1016/j.imavis.2004.06.013

I. Bloch, T. Géraud, and H. Ma??trema??tre, Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition???Application to 3D brain imaging, Artificial Intelligence, vol.148, issue.1-2, pp.141-175, 2003.
DOI : 10.1016/S0004-3702(03)00018-3

URL : https://hal.archives-ouvertes.fr/hal-00556174

J. Bonneville, F. Bonneville, C. , and F. , Magnetic resonance imaging of pituitary adenomas, European Radiology, vol.170, issue.3, pp.543-548, 2005.
DOI : 10.1007/s00330-004-2531-x

M. A. Brown and R. C. Semelka, MRI: Basic Principles and Applications, 2003.
DOI : 10.1002/0471467936

C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

A. Burgun, Desiderata for domain reference ontologies in biomedicine, Journal of Biomedical Informatics, vol.39, issue.3, pp.307-313, 2006.
DOI : 10.1016/j.jbi.2005.09.002

C. Busch, Wavelet based texture segmentation of multi-modal tomographic images, Computers & Graphics, vol.21, issue.3, pp.347-358, 1997.
DOI : 10.1016/S0097-8493(97)00012-5

J. T. Bushberg, A. Seibert, E. M. Leidholdt, and J. M. Boone, The Essential Physics of Medical Imaging, Lippincott Williams and Wilkins, 2002.
DOI : 10.1118/1.1585033

H. Cai, R. Verma, Y. Ou, S. Lee, E. R. Melhem et al., PROBABILISTIC SEGMENTATION OF BRAIN TUMORS BASED ON MULTI-MODALITY MAGNETIC RESONANCE IMAGES, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.600-603, 2007.
DOI : 10.1109/ISBI.2007.356923

A. S. Capelle, O. Colot, and C. Fernandez-maloigne, Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Information Fusion, pp.203-216, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00336552

R. Casati, . Smith, and A. C. Varzi, Ontological tools for geographic representation, Japanese translation in Inter Communication, pp.80-91, 2003.
URL : https://hal.archives-ouvertes.fr/ijn_00000095

V. Caselles, F. Catte, T. Coll, D. , and F. , Geometric models for active contours, Proceedings., International Conference on Image Processing, pp.1-31, 1993.
DOI : 10.1109/ICIP.1995.537567

J. E. Cates, A. E. Lefohn, W. , and R. T. , GIST: an interactive, GPU-based level set segmentation tool for 3D medical images, Medical Image Analysis, vol.8, issue.3, pp.217-231, 2004.
DOI : 10.1016/j.media.2004.06.022

S. Chaplot, L. M. Patnaik, and N. R. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control, vol.1, issue.1, pp.86-92, 2006.
DOI : 10.1016/j.bspc.2006.05.002

T. Chen and D. N. Metaxas, Gibbs Prior Models, Marching Cubes, and Deformable Models: A Hybrid Framework for 3D Medical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 2879 of LNCS, pp.703-710, 2003.
DOI : 10.1007/978-3-540-39903-2_86

C. Ciofolo, C. Barillot, and P. Hellier, Combining fuzzy logic and level set methods for 3D MRI brain segmentation, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.161-164, 2004.
DOI : 10.1109/ISBI.2004.1398499

M. Clark, Knowledge-Guided Processing of Magnetic Resonance Images of the Brain, 1997.

M. C. Clark, L. O. Lawrence, D. B. Golgof, R. Velthuizen, F. R. Murtagh et al., Automatic tumor segmentation using knowledge-based techniques, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.187-201, 1998.
DOI : 10.1109/42.700731

O. Clatz, M. Sermesant, P. Bondiau, H. Delingette, S. K. Warfield et al., Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with mass effect, IEEE Transactions on Medical Imaging, issue.10, pp.241334-1346, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00615662

C. A. Cocosco, V. Kollokian, R. K. Kwan, and A. C. Evans, Brainweb: Online interface to a 3D MRI simulated brain database, NeuroImage (Proceedings of 3-rd International Conference on Functional Mapping of the Human Brain), p.425, 1997.

Y. Cointepas, J. Mangin, L. Garnero, J. Poline, and H. Benali, Brain VISA: Software platform for visualization and analysis of multi-modality brain data, Neuroimage, issue.6, pp.13-98, 2001.

D. L. Collins, 3D Model-based segmentation of individual brain structures from magnetic resonance imaging data, 1994.

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, pp.1401-1414, 2006.
DOI : 10.1016/j.patcog.2006.02.022

URL : https://hal.archives-ouvertes.fr/hal-00878443

T. F. Cootes, D. Cooper, C. J. Taylor, G. , and J. , Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, issue.1, pp.6138-59, 1995.
DOI : 10.1006/cviu.1995.1004

O. Corcho, M. Fernandez-lopez, and A. Gomez-perez, Methodologies, tools and languages for building ontologies: where is their meeting point? Data Knowledge Engineering, pp.41-64, 2003.
DOI : 10.1016/s0169-023x(02)00195-7

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

J. J. Corso, E. Sharon, Y. , and A. , Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation, MICCAI2006, volume LNCS 4191, pp.790-798, 2006.
DOI : 10.1007/11866763_97

C. Cortes and V. N. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

M. Cristani and R. Cuel, A Survey on Ontology Creation Methodologies, International Journal on Semantic Web and Information Systems, vol.1, issue.2, pp.49-69, 2005.
DOI : 10.4018/jswis.2005040103

J. G. Curran, O. , and E. , Imaging of craniopharyngioma. Child's Nervous System, pp.635-639, 2005.

E. Dam and M. L. Letteboer, Integrating automatic and interactive brain tumor segmentation, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.790-793, 2004.
DOI : 10.1109/ICPR.2004.1334647

O. Dameron, B. Gibaud, and X. Morandi, Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results, Surgical and Radiologic Anatomy, vol.26, issue.3, pp.191-197, 2004.
DOI : 10.1007/s00276-003-0204-0

URL : https://hal.archives-ouvertes.fr/inserm-00152193

S. Dasiopoulou, V. Mezaris, I. Kompatsiaris, V. K. Papastathis, and M. G. Strintzis, Knowledge-assisted semantic video object detection, IEEE Transactions on Circuits and Systems for Video Technology, pp.151210-1224, 2005.
DOI : 10.1109/TCSVT.2005.854238

J. Dauguet, J. F. Mangin, T. Delzescaux, and V. Frouin, Robust interslice intensity normalization using histogram scale-space analysis, LNCS, vol.3216, pp.242-249, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00615980

C. Daumas-duport, Histological grading of gliomas, Current Opinion in Neurology and neurosurgery, vol.5, issue.6, pp.924-931, 1992.

C. Daumas-duport, F. Beuvon, P. Varlet, and C. Fallet-bianco, Gliomes: Classification de l'OMS et de l'Ho?pitalHo?pital Sainte-Anne, Ann. Pathology, vol.20, issue.5, pp.413-428

B. Dawant, S. Hartmann, J. Thirion, F. Maes, D. Vandermeulen et al., Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects, IEEE Transactions on Medical Imaging, vol.18, issue.10, p.18, 1999.
DOI : 10.1109/42.811271

B. M. Dawant, S. L. Hartmann, and S. Gadamsetty, Brain Atlas Deformation in the Presence of Large Space-occupying Tumors, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.589-596, 1999.
DOI : 10.1007/10704282_63

M. Dawant, S. L. Hartmann, S. Pan, and S. Gadamsetty, Brain Atlas Deformation in the Presence of Small and Large Space-Occupying Tumors, Computer Aided Surgery, vol.7, issue.1, pp.1-10, 2002.
DOI : 10.1109/42.700731

H. Delingette, General object reconstruction based on simplex meshes, International Journal of Computer Vision, vol.32, issue.2, pp.111-146, 1999.
DOI : 10.1023/A:1008157432188

URL : https://hal.archives-ouvertes.fr/inria-00073579

S. Dickson, B. T. Thomas, G. , and P. , Using Neural Networks to Automatically Detect Brain Tumours in MR Images, International Journal of Neural Systems, vol.08, issue.01, pp.91-99, 1997.
DOI : 10.1142/S0129065797000124

P. F. Dominey, J. D. Boucher, and T. Inui, Building an adaptive spoken language interface for perceptually grounded human-robot interaction, 4th IEEE/RAS International Conference on Humanoid Robots, 2004., pp.168-183, 2004.
DOI : 10.1109/ICHR.2004.1442121

M. Donnelly, T. Bittner, R. , and C. , A formal theory for spatial representation and reasoning in biomedical ontologies, Artificial Intelligence in Medicine, vol.36, issue.1, pp.1-27, 2006.
DOI : 10.1016/j.artmed.2005.07.004

N. D. Doolittle, State of the science in brain tumor classification, Seminars in Oncology Nursing, pp.224-230, 2004.
DOI : 10.1016/S0749-2081(04)00086-5

W. Dou, S. Ruan, Y. Chen, D. Bloyet, C. et al., A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images, Image and Vision Computing, vol.25, issue.2, pp.164-171, 2007.
DOI : 10.1016/j.imavis.2006.01.025

URL : https://hal.archives-ouvertes.fr/hal-00673700

M. Droske, B. Meyer, M. Rumpf, and C. Schaller, An Adaptive Level Set Method for Medical Image Segmentation, Proceedings of the 17th International Conference on Information Processing in Medical Imaging, pp.416-422, 2001.
DOI : 10.1007/3-540-45729-1_43

D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, 1980.

N. Duta and M. Sonka, Segmentation and interpretation of MR brain images. An improved active shape model, IEEE Transactions on Medical Imaging, vol.17, issue.6, pp.1049-1062, 1998.
DOI : 10.1109/42.746716

H. H. Engelhard, Progress in the diagnosis and treatment of patients with meningiomas, Surgical Neurology, vol.55, issue.2, pp.89-101, 2001.
DOI : 10.1016/S0090-3019(01)00349-4

H. H. Engelhard, A. Stelea, and A. Mundt, Oligodendroglioma and anaplastic oligodendroglioma:, Surgical Neurology, vol.60, issue.5, pp.443-456, 2003.
DOI : 10.1016/S0090-3019(03)00167-8

Y. Feng and W. Chen, Brain MR Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field Theory, Second International Workshop on Medical Imaging and Augmented Reality (MIAR), pp.188-195, 2004.
DOI : 10.1007/978-3-540-28626-4_23

F. Firenze and P. Morasso, The capture effect model: a new approach to selforganized clustering, The Sixth International Conference on Neural Networks and their Industrial and Cognitive Applications and Exhibition Catalog, NEURO-NIMES 93, pp.45-54, 1993.

B. Fischl, D. Salat, E. Busa, M. Albert, M. Dieterich et al., Whole brain segmentation. Automated labeling of neuroanatomical structures in the human brain, Neuron, issue.3, pp.33341-355, 2002.

L. M. Fletcher-heath, L. O. Hall, D. B. Goldgof, and F. Murtagh, Automatic segmentation of non-enhancing brain tumors in magnetic resonance images, Artificial Intelligence in Medicine, vol.21, issue.1-3, pp.43-63, 2001.
DOI : 10.1016/S0933-3657(00)00073-7

G. Fouquier, J. Atif, and I. Bloch, Local Reasoning in Fuzzy Attribute Graphs for Optimizing Sequential Segmentation, 6th IAPR-TC15 Workshop on Graphbased Representations in Pattern Recognition, GbR'07, pp.138-147, 2007.
DOI : 10.1007/978-3-540-72903-7_13

J. Freeman, The modelling of spatial relations, Computer Graphics and Image Processing, vol.4, issue.2, pp.156-171, 1975.
DOI : 10.1016/S0146-664X(75)80007-4

C. Garcia and J. Moreno, Kernel Based Method for Segmentation and Modeling of Magnetic Resonance Images, LNCS, vol.3315, pp.636-645, 2004.
DOI : 10.1007/978-3-540-30498-2_64

T. Geraud, Segmentation des structures internes du cerveau en imagerie par résonance magnétique tridimensionnelle, 1998.

G. Gerig, M. Jomier, C. , and M. , Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation, MICCAI, pp.516-523, 2001.
DOI : 10.1007/3-540-45468-3_62

D. T. Gering, Recognizing Deviations from Normalcy for Brain Tumor Segmentation, 2003.
DOI : 10.1007/3-540-45786-0_48

P. Gibbs, D. L. Buckley, S. J. Blackband, and A. Horsman, Tumour volume determination from MR images by morphological segmentation, Physics in Medicine and Biology, vol.41, issue.11, pp.412437-2446, 1996.
DOI : 10.1088/0031-9155/41/11/014

A. Gomez-perez, M. Fernandez-lopez, C. , and O. , Ontological Engineering, 2004.

W. E. Grimson and P. Golland, Analyzing Anatomical Structures: Leveraging Multiple Sources of Knowledge, CVBIA, pp.3-12, 2005.
DOI : 10.1007/11569541_2

T. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisition, vol.5, issue.2, pp.199-220, 1993.
DOI : 10.1006/knac.1993.1008

E. M. Haacke, R. W. Brown, M. R. Thompson, and R. Venkatesan, Magnetic Resonance Imaging: Physical Principles and Sequence Design, 1999.

V. Haarslev and R. Moller, RACER System Description, International Joint Conference on Automated Reasoning, pp.701-706, 2001.
DOI : 10.1007/3-540-45744-5_59

J. V. Hajnal, N. Saeed, E. J. Soar, A. Oatridge, R. I. Young et al., A Registration and Interpolation Procedure for Subvoxel Matching of Serially Acquired MR Images, Journal of Computer Assisted Tomography, vol.19, issue.2, pp.289-296, 1995.
DOI : 10.1097/00004728-199503000-00022

L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger et al., A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Transactions on Neural Networks, vol.3, issue.5, pp.672-682, 1992.
DOI : 10.1109/72.159057

D. Han, B. J. You, Y. S. Kim, and I. L. Suh, A Generic Shape Matching with Anchoring of Knowledge Primitives of Object Ontology, ICIAR, pp.437-480, 2005.
DOI : 10.1007/11559573_59

N. Hata, Y. Muragaki, T. Inomata, T. Maruyama, H. Iseki et al., Intraoperative tumor segmentation and volume measurement in MRI-guided glioma surgery for tumor resection rate control1, Academic Radiology, vol.12, issue.1, pp.116-122, 2005.
DOI : 10.1016/j.acra.2004.11.009

K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis et al., Markov random field segmentation of brain MR images, IEEE Transactions on Medical Imaging, vol.16, issue.6, pp.16878-886, 1997.
DOI : 10.1109/42.650883

J. W. Henson, P. Gaviani, and R. G. Gonzalez, MRI in treatment of adult gliomas, The Lancet Oncology, vol.6, issue.3, pp.167-175, 2005.
DOI : 10.1016/S1470-2045(05)01767-5

S. Herlidou-meme, J. M. Constans, B. Carsin, D. Olivie, P. A. Eliat et al., MRI texture analysis on texture test objects, normal brain and intracranial tumors, Magnetic Resonance Imaging, vol.21, issue.9, pp.989-993, 2003.
DOI : 10.1016/S0730-725X(03)00212-1

H. E. Herskovits, R. Itoh, and E. R. Melhem, Accuracy for Detection of Simulated Lesions, American Journal of Roentgenology, vol.176, issue.5, pp.1313-1318, 2001.
DOI : 10.2214/ajr.176.5.1761313

S. Ho, E. Bullitt, and G. Gerig, Level-set evolution with region competition: automatic 3-D segmentation of brain tumors, Object recognition supported by user interaction for service robots, pp.532-535, 2002.
DOI : 10.1109/ICPR.2002.1044788

F. Hoppner, A contribution to convergence theory of fuzzy c-means and derivatives, IEEE Transactions on Fuzzy Systems, vol.11, issue.5, pp.682-694, 2003.
DOI : 10.1109/TFUZZ.2003.817858

S. Hu and D. L. Collins, Joint level-set shape modeling and appearance modeling for brain structure segmentation, NeuroImage, vol.36, issue.3, pp.672-683, 2007.
DOI : 10.1016/j.neuroimage.2006.12.048

C. Hudelot, J. Atif, and I. Bloch, Fuzzy Spatial Relation Ontology for Image Interpretation. Fuzzy Sets and Systems, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00824590

K. M. Iftekharuddin, W. Jia, and R. Marsh, Fractal analysis of tumor in brain MR images, Machine Vision and Applications, pp.352-362, 2003.
DOI : 10.1007/s00138-002-0087-9

D. V. Iosifescu, M. E. Shenton, S. K. Warfield, R. Kikinis, J. Dengler et al., An Automated Registration Algorithm for Measuring MRI Subcortical Brain Structures, NeuroImage, vol.6, issue.1, pp.13-25, 1997.
DOI : 10.1006/nimg.1997.0274

A. K. Jain, M. N. Murty, F. , and P. J. , Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.31264-323, 1999.
DOI : 10.1145/331499.331504

M. Jenkinson, P. R. Bannister, J. M. Brady, S. , and S. M. , Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images, NeuroImage, vol.17, issue.2, pp.825-841, 2002.
DOI : 10.1006/nimg.2002.1132

C. Jiang, X. Xinhaua, W. Huang, and C. Mene, Segmentation and quantification of brain tumor, IEEE Symposium on Virtual Environments, Human- Computer Interfaces and Measurement Systems (VECIMS), pp.61-66, 2004.

G. W. Jiji and L. Ganesan, Unsupervised segmentation using fuzzy logic based texture spectrum for MRI brain images, Third World Enformatika Conference (WEC2005), pp.155-157, 2005.

M. Julì-a-sapé, D. Acosta, C. Majós, A. Moreno-torres, P. Wesseling et al., Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database, Journal of Neurosurgery, vol.105, issue.1, pp.6-14
DOI : 10.3171/jns.2006.105.1.6

G. Kantor, H. Loiseau, A. Vital, and J. J. And-mazeron, Volume tumoral macroscopique (GTV) et volume???cible anatomoclinique (CTV) des tumeurs gliales de l???adulte, Cancer/Radioth??rapie, vol.5, issue.5, pp.581-580, 2001.
DOI : 10.1016/S1278-3218(01)00107-X

M. Kass, A. Witkin, T. , and D. , Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988.
DOI : 10.1007/BF00133570

M. R. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz et al., Automated Segmentation of MR Images of Brain Tumors, Radiology, vol.218, issue.2, pp.586-591, 2001.
DOI : 10.1148/radiology.218.2.r01fe44586

M. R. Kaus, S. K. Warfield, A. Nabavi, E. Chatzidakis, P. M. Black et al., Segmentation of Meningiomas and Low Grade Gliomas in MRI, MICCAI, volume LNCS 1679, pp.1-10, 1999.
DOI : 10.1007/10704282_1

A. Kelemen, G. Szekely, and G. Gerig, Elastic model-based segmentation of 3-D neuroradiological data sets, IEEE Transactions on Medical Imaging, vol.18, issue.10, pp.828-839, 1999.
DOI : 10.1109/42.811260

J. W. Kernohan, R. F. Maybon, H. J. Svien, and A. W. Adson, A simplified classification of the gliomas, Proc Staff Meet Mayo Clin, vol.24, pp.71-75, 1949.

H. Khotanlou, J. Atif, E. Angelini, H. Duffau, and I. Bloch, ADAPTIVE SEGMENTATION OF INTERNAL BRAIN STRUCTURES IN PATHOLOGICAL MR IMAGES DEPENDING ON TUMOR TYPES, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.588-591, 2007.
DOI : 10.1109/ISBI.2007.356920

H. Khotanlou, J. Atif, O. Colliot, and I. Bloch, 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models, WILF2005, volume 3849 of Lecture notes in computer science(LNCS), pp.312-318, 2005.
DOI : 10.1007/11676935_39

URL : https://hal.archives-ouvertes.fr/hal-01251278

H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, Brain tumor detection and segmentation using fuzzy classification, symmetry analysis and deformable model. Fuzzy Sets and Systems, 2007.

H. Khotanlou, O. Colliot, and I. Bloch, Automatic Brain Tumor Segmentation Using Symmetry Analysis and Deformable Models, Advances in Pattern Recognition, pp.198-202, 2007.
DOI : 10.1142/9789812772381_0032

L. Kjaer, P. Ring, C. Thomsen, and O. Henriksen, Texture analysis in quantitative MR imaging. Tissue characterization of normal brain and intracranial tumours at 1.5 T, Acta Radiologica, vol.36, issue.2, pp.127-135, 1995.

E. Klien and M. Lutz, The Role of Spatial Relations in Automating the Semantic Annotation of Geodata, Conference on Spatial Information Theory, pp.133-148, 2005.
DOI : 10.1007/11556114_9

R. Krishnapuram and J. M. Keller, A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems, vol.1, issue.2, pp.98-110, 1993.
DOI : 10.1109/91.227387

R. Krishnapuram and J. M. Keller, The possibilistic C-means algorithm: insights and recommendations, IEEE Transactions on Fuzzy Systems, vol.4, issue.3, pp.385-393, 1996.
DOI : 10.1109/91.531779

F. Kruggel and G. Lohmann, Automatical adaption of the stereotactical coordinate system in brain MRI datasets, International Conference on Information Processing in Medical Imaging (IPMI), pp.471-476, 1997.
DOI : 10.1007/3-540-63046-5_45

D. W. Kufe, R. E. Pollock, R. R. Weichselbaum, T. S. Gansler, and R. Bast, Holland-Frei Cancer Medicine, 2003.

S. K. Kyriacou, C. Davatzikos, S. J. Zinreich, and N. Bryan, Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI], IEEE Transactions on Medical Imaging, vol.18, issue.7, pp.18580-592, 1999.
DOI : 10.1109/42.790458

A. K. Law, F. K. Lam, C. , and F. H. , A fast deformable region model for brain tumor boundary extraction, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, pp.1055-1056, 2002.
DOI : 10.1109/IEMBS.2002.1106273

A. K. Law, H. Zhu, B. C. Chan, P. P. Iu, F. K. Lam et al., Semi-automatic tumor boundary detection in MR image sequences, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489), pp.28-31, 2001.
DOI : 10.1109/ISIMP.2001.925322

A. G. Lee, P. W. Brazis, J. A. Garrity, and M. White, Imaging for neuro-ophthalmic and orbital disease, American Journal of Ophthalmology, vol.138, issue.5, pp.852-863, 2004.
DOI : 10.1016/j.ajo.2004.06.069

C. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander et al., Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines, LNCS, pp.469-478, 2005.
DOI : 10.1007/11569541_47

K. V. Leemput, F. Maes, D. Vandermeulen, A. Colchester, and P. Suetens, Automated segmentation of multiple sclerosis lesions by model outlier detection, IEEE Transactions on Medical Imaging, vol.20, issue.8, pp.677-688, 2001.
DOI : 10.1109/42.938237

A. Lefohn, J. Cates, W. , and R. , Interactive, GPU-based level sets for 3D brain tumor segmentation, 2003.

R. A. Lerski, K. Straughan, L. R. Schad, D. Boyce, S. Bluml et al., VIII. MR image texture analysis???An approach to tissue characterization, Magnetic Resonance Imaging, vol.11, issue.6, pp.11873-887, 1993.
DOI : 10.1016/0730-725X(93)90205-R

M. Letteboer, O. Olsen, E. Dam, P. Willems, M. Viergever et al., Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm, Academic Radiology, issue.10, pp.111125-1138, 2004.

A. W. Liew, H. Yan, and H. , An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation, IEEE Transactions on Medical Imaging, vol.22, issue.9, pp.1063-1075, 2003.
DOI : 10.1109/TMI.2003.816956

M. G. Linguraru, M. A. Gonzalez, and N. Ayache, Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging, International Journal of Computers Communications & Control, vol.2, issue.1, pp.26-36, 2007.
DOI : 10.15837/ijccc.2007.1.2333

URL : https://hal.archives-ouvertes.fr/inria-00616025

J. Liu, J. K. Udupa, D. Odhner, D. Hackney, and G. Moonis, A system for brain tumor volume estimation via MR imaging and fuzzy connectedness, Computerized Medical Imaging and Graphics, vol.29, issue.1, pp.21-34, 2005.
DOI : 10.1016/j.compmedimag.2004.07.008

Y. Liu, R. T. Collins, R. , and W. E. , Automatic extraction of the central symmetry (mid-sagittal) plane from neuroradiology images, 1996.

R. D. Lobato, R. Alday, P. A. Gomez, J. J. Rivas, J. Dominguez et al., Brain oedema in patients with intracranial meningioma, Acta Neurochirurgica, vol.125, issue.5, pp.485-495, 1996.
DOI : 10.1007/BF01411166

M. B. Lopes and E. R. Laws, Low-grade central nervous system tumors, Neurosurgical Focus, vol.12, issue.2, pp.1-4, 2002.
DOI : 10.3171/foc.2002.12.2.2

S. Luo, R. Li, and S. Ourselin, A new deformable model using dynamic gradient vector flow and adaptive balloon forces, APRS Workshop on Digital Image Computing, 2003.

L. Ma and R. C. Staunton, A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns, Pattern Recognition, vol.40, issue.11, pp.403005-3011, 2007.
DOI : 10.1016/j.patcog.2007.02.005

V. A. Magnotta, H. J. Bockholt, H. J. Johnson, G. E. Christensen, and N. C. Andreasen, Subcortical, cerebellar, and magnetic resonance based consistent brain image registration, NeuroImage, vol.19, issue.2, pp.233-245, 2003.
DOI : 10.1016/S1053-8119(03)00100-9

M. R. Mahesh and V. Tse, Diagnosis and staging of brain tumors, Seminars in Roentgenology, pp.347-360, 2004.

D. Mahmoud-ghoneima, G. Toussaintb, J. Constansc, and J. D. De-certainesa, Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas, Magnetic Resonance Imaging, vol.21, issue.9, pp.983-987, 2003.
DOI : 10.1016/S0730-725X(03)00201-7

N. E. Maillot and M. Thonnat, Ontology based complex object recognition. Image and Vision Computing, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00502361

J. Maintz and M. Viergever, A survey of medical image registration, Medical Image Analysis, vol.2, issue.1, pp.1-36, 1998.
DOI : 10.1016/S1361-8415(01)80026-8

Y. A. Maksoud, Y. S. Hahn, and H. H. Engelhard, Intracranial ependymoma, Neurosurgical Focus, vol.13, issue.3, pp.1-5, 2002.
DOI : 10.3171/foc.2002.13.3.5

R. Malladi, J. Sethian, and B. 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

M. Mancas and B. Gosselin, Towards an automatic tumor segmentation using iterative watersheds, Medical Imaging 2004: Image Processing, pp.1598-1608, 2004.
DOI : 10.1117/12.535017

J. Mangin, Entropy minimization for automatic correction of intensity nonuniformity, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737), pp.162-169, 2000.
DOI : 10.1109/MMBIA.2000.852374

J. Mangin, O. Coulon, and V. Frouin, Robust brain segmentation using histogram scale-space analysis and mathematical morphology, MICCAI, pp.1230-1241, 1998.
DOI : 10.1109/34.19041

E. N. Marieb, Human Anatomy and Physiology, 2000.

G. Marquet, O. Dameron, S. Saikali, J. Mosser, and A. Burgun, Grading glioma tumors using OWL-DL and NCI thesaurus, Proceedings of the American Medical Informatics Association Conference AMIA'07, 2007.

F. Masulli and A. Schenone, A fuzzy clustering based segmentation system as support to diagnosis in medical imaging, Artificial Intelligence in Medicine, vol.16, issue.2, pp.129-147, 1999.
DOI : 10.1016/S0933-3657(98)00069-4

E. Meijering, A chronology of interpolation: From ancient astronomy to modern signal and image processing, Proceedings of the IEEE, pp.319-342, 2002.

A. Mohamed, D. Shen, D. , and C. , Deformable registration of brain tumor images via a statistical model of tumor-induced deformation, MICCAI, pp.263-270, 2005.

N. Moon, E. Bullitt, K. V. Leemput, and G. Gerig, Model-based brain and tumor segmentation, Object recognition supported by user interaction for service robots, pp.528-531, 2002.
DOI : 10.1109/ICPR.2002.1044787

T. K. Moon, The expectation-maximization algorithm, IEEE Signal Processing Magazine, vol.13, issue.6, pp.47-60, 1996.
DOI : 10.1109/79.543975

G. Moonis, J. Liu, J. K. Udupa, and D. B. Hackney, Estimation of tumor volume with fuzzy-connectedness segmentation of MR images, American Journal of Neuroradiology, vol.23, pp.352-363, 2002.

D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M. Shenton et al., STATISTICAL SHAPE ANALYSIS OF BRAIN STRUCTURES USING SPHERICAL WAVELETS, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.209-212, 2007.
DOI : 10.1109/ISBI.2007.356825

O. Nempont, J. Atif, E. Angelini, and I. Bloch, Combining Radiometric and Spatial Structural Information in a New Metric for Minimal Surface Segmentation, Information Processing in Medical Imaging, pp.283-295, 2007.
DOI : 10.1007/978-3-540-73273-0_24

M. Nientiedt, Remote Sensing Image Mining: Applying Action-Driven Ontologies to the Change of Landuse Patterns, 2007.

X. Niu, A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model, ISPRS Journal of Photogrammetry and Remote Sensing, vol.61, issue.3-4, pp.170-186, 2006.
DOI : 10.1016/j.isprsjprs.2006.08.004

W. Nowinski and D. Belov, Toward Atlas-Assisted Automatic Interpretation of MRI Morphological Brain Scans in the Presence of Tumor, Academic Radiology, vol.12, issue.10, pp.1049-1057, 2005.
DOI : 10.1016/j.acra.2005.08.030

S. Osher and J. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988.
DOI : 10.1016/0021-9991(88)90002-2

N. R. Pal, K. Pal, and J. C. Bezdek, A mixed c-means clustering model, Proceedings of 6th International Fuzzy Systems Conference, pp.11-21, 1997.
DOI : 10.1109/FUZZY.1997.616338

N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, A possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems, vol.13, issue.4, pp.517-530, 2005.
DOI : 10.1109/TFUZZ.2004.840099

Z. Peng, W. Wee, L. , and J. H. , MR brain imaging segmentation based on spatial Gaussian mixture model and Markov random field, IEEE International Conference on Image Processing (ICIP), pp.313-316, 2005.

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

D. L. Pham, Spatial Models for Fuzzy Clustering, Computer Vision and Image Understanding, vol.84, issue.2, pp.285-297, 2001.
DOI : 10.1006/cviu.2001.0951

D. L. Pham, C. Xu, P. , and J. L. , A survey current methods in segmentation, Annual Review of Biomedical Engineering, pp.315-337, 2000.

W. E. Phillips, R. P. Velthuizen, S. Phuphanich, L. O. Hall, L. P. Clarke et al., Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme, Magnetic Resonance Imaging, vol.13, issue.2, pp.277-290, 1995.
DOI : 10.1016/0730-725X(94)00093-I

B. Piquet, C. T. Silva, and A. Kaufman, Tetra-cubes: an algorithm to generate 3D isosurfaces based upon tetrahedra, Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 96, pp.205-210, 1996.

A. Pitiot, A. W. Toga, and P. M. Thompson, Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming, IEEE Transactions on Medical Imaging, vol.21, issue.8, pp.910-923, 2002.
DOI : 10.1109/TMI.2002.803124

URL : https://hal.archives-ouvertes.fr/inria-00615626

S. R. Plotkin and T. T. Batchelor, Primary nervous-sytem lymphoma. The Lancet Oncology, pp.354-365, 2001.

K. M. Pohl, W. E. Grimson, S. Bouix, and R. Kikinis, Anatomical guided segmentation with non-stationary tissue class distributions in an expectationmaximization framework, IEEE International Symposium on Biomedical Imaging: Nano to Macro (ISBI), pp.81-84, 2004.

C. Pollo, M. Bach-cuadra, O. Cuisenaire, J. Villemure, and J. P. Thiran, Segmentation of brain structures in presence of a space-occupying lesion, NeuroImage, vol.24, issue.4, pp.990-996, 2005.
DOI : 10.1016/j.neuroimage.2004.10.004

F. Poupon, J. Mangin, D. Hasboun, C. Poupon, I. Magnin et al., Multi-object deformable templates dedicated to the segmentation of brain deep structures, MICCAI, volume 1496 of LNCS, pp.1134-1143, 1998.
DOI : 10.1109/34.42836

S. Powell, V. A. Magnotta, H. J. Johnson, V. K. Jammalamadaka, and N. C. Andreasen, Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures, NeuroImage, vol.39, issue.1, 2008.
DOI : 10.1016/j.neuroimage.2007.05.063

M. Prastawa, E. Bullitt, and G. Gerig, Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI), pp.26-33, 2005.
DOI : 10.1007/11566465_4

M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, A brain tumor segmentation framework based on outlier detection*1, Medical Image Analysis, vol.8, issue.3, pp.217-231, 2004.
DOI : 10.1016/j.media.2004.06.007

M. Prastawa, E. Bullitt, N. Moon, K. V. Leemput, and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors1, Academic Radiology, vol.10, issue.12, pp.1341-1348, 2003.
DOI : 10.1016/S1076-6332(03)00506-3

W. E. Reddick, R. K. Mulhern, T. D. Elkin, J. O. Glass, T. E. Merchant et al., A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors, Magnetic Resonance Imaging, vol.16, issue.4, pp.413-421, 1998.
DOI : 10.1016/S0730-725X(98)00014-9

J. Rexilius, H. K. Hahn, J. Klein, M. G. Lentschig, and H. Peitgen, Multispectral brain tumor segmentation based on histogram model adaptation, Medical Imaging 2007: Computer-Aided Diagnosis, p.65140, 2007.
DOI : 10.1117/12.709410

P. E. Ricci and D. H. Dungan, Imaging of low- and intermediate-grade gliomas, Seminars in Radiation Oncology, vol.11, issue.2, pp.103-112103, 2001.
DOI : 10.1053/srao.2001.21420

J. Ringertz, ??GRADING?? OF GLIOMAS, Acta Pathologica Microbiologica Scandinavica, vol.4, issue.1, pp.27-51, 1950.
DOI : 10.1111/j.1699-0463.1950.tb05192.x

A. Roche, G. Malandain, X. Pennec, and N. Ayache, The correlation ratio as a new similarity measure for multimodal image registration, Lecture Notes in Computer Science, vol.17, issue.4, pp.1115-1124, 1998.
DOI : 10.1097/00004728-199307000-00004

URL : https://hal.archives-ouvertes.fr/cea-00333675

T. Rohlfing, R. Brandt, R. Menzel, and C. R. Maurer, Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains, NeuroImage, vol.21, issue.4, pp.1428-1442, 2004.
DOI : 10.1016/j.neuroimage.2003.11.010

C. Rosse and J. L. Mejino, A reference ontology for biomedical informatics: the Foundational Model of Anatomy, Journal of Biomedical Informatics, vol.36, issue.6, pp.478-500, 2003.
DOI : 10.1016/j.jbi.2003.11.007

S. Ruan, S. Lebonvallet, A. Merabet, C. , and J. , TUMOR SEGMENTATION FROM A MULTISPECTRAL MRI IMAGES BY USING SUPPORT VECTOR MACHINE CLASSIFICATION, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1236-1239, 2007.
DOI : 10.1109/ISBI.2007.357082

D. S. Russel and L. J. Rubinstein, Pathology of tumors of the nervous system, pp.147-153, 1971.

M. Sabin, Convergence and Consistency of Fuzzy c-means/ISODATA Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.5, pp.661-668, 1987.
DOI : 10.1109/TPAMI.1987.4767960

P. K. Saha and J. K. Udupa, Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation, Computer Vision and Image Understanding, vol.82, issue.1, pp.42-56, 2001.
DOI : 10.1006/cviu.2000.0902

A. Saleh, F. Wenserski, M. Cohnen, G. Furst, E. Godehardt et al., Exclusion of brain lesions: is MR contrast medium required after a negative fluid-attenuated inversion recovery sequence?, The British Journal of Radiology, vol.77, issue.915, pp.183-188, 2004.
DOI : 10.1259/bjr/62546157

L. R. Schad, S. Bluml, and I. Zuna, IX. MR tissue characterization of intracranial tumors by means of texture analysis, Magnetic Resonance Imaging, vol.11, issue.6, pp.889-896, 1993.
DOI : 10.1016/0730-725X(93)90206-S

M. Schmidt, I. Levner, R. Greiner, A. Murtha, and A. Bistritz, Segmenting Brain Tumors using Alignment-Based Features, Fourth International Conference on Machine Learning and Applications (ICMLA'05), pp.215-220, 2005.
DOI : 10.1109/ICMLA.2005.56

P. Schroeter, J. Vesin, T. Langenberger, and R. Meuli, Robust parameter estimation of intensity distributions for brain magnetic resonance images, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.172-186, 1998.
DOI : 10.1109/42.700730

S. Schulz, U. Hahn, and M. Romacker, Modeling anatomical spatial relations with description logics, Annual Symposium of the American Medical Informatics Association. Converging Information, Technology, and Health Care, pp.779-783, 2000.

J. Serra, Image analysis and mathematical morphology, 1982.

E. Sharon, A. Brandt, and R. Basri, Segmentation and boundary detection using multiscale intensity measurements, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.469-476, 2001.
DOI : 10.1109/CVPR.2001.990512

D. W. Shattuck and R. M. Leahy, BrainSuite: An automated cortical surface identification tool, Medical Image Analysis, vol.6, issue.2, pp.129-142, 2002.
DOI : 10.1016/S1361-8415(02)00054-3

D. W. Shattuck, S. R. Sandor-leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, Magnetic Resonance Image Tissue Classification Using a Partial Volume Model, NeuroImage, vol.13, issue.5, pp.13856-876, 2001.
DOI : 10.1006/nimg.2000.0730

D. Shen, E. Herskovits, D. , and C. , An adaptative-focus statistical shape model for segmentation and shape modeling of 3D brain structures, IEEE Transactions on Medical Imaging, vol.20, issue.4, 2001.

S. Shen, W. Sandham, M. Granat, and A. Sterr, MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization, IEEE Transactions on Information Technology in Biomedicine, vol.9, issue.3, pp.459-467, 2005.
DOI : 10.1109/TITB.2005.847500

S. Shen, W. A. Sandham, and M. H. Granat, Preprocessing and segmentation of brain magnetic resonance images, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003., pp.149-152, 2003.
DOI : 10.1109/ITAB.2003.1222495

P. Shichun, L. Jian, and Y. Guoping, Neural Integration Approach for Subcortical Structure Segmentation, 2005 International Conference on Neural Networks and Brain, pp.244-248, 2005.
DOI : 10.1109/ICNNB.2005.1614607

J. G. Sled and G. B. Pike, Understanding intensity non-uniformity in MRI, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.614-622, 1998.
DOI : 10.1097/00004728-199403000-00005

J. G. Smirniotopoulos, The new WHO classification of brain tumors, Neuroimaging Clin N Am, vol.9, issue.4, pp.595-613, 1999.

S. M. Smith, Fast robust automated brain extraction, Human Brain Mapping, vol.20, issue.3, pp.143-155, 2002.
DOI : 10.1002/hbm.10062

S. M. Smith, P. R. Bannister, C. F. Beckmann, J. M. Brady, S. Clare et al., FSL: New tools for functional and structural brain image analysis, International Conference on Functional Mapping of the Human Brain, p.249, 2001.
DOI : 10.1016/S1053-8119(01)91592-7

S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. Behrens et al., Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, vol.23, issue.S1, pp.23208-219, 2004.
DOI : 10.1016/j.neuroimage.2004.07.051

J. Solomon, J. A. Butman, and A. Sood, Segmentation of brain tumors in 4D MR images using the hidden Markov model, Computer Methods and Programs in Biomedicine, vol.84, issue.2-3, pp.76-85, 2006.
DOI : 10.1016/j.cmpb.2006.09.007

H. Soltanian-zadeh, M. Kharrat, D. , and P. J. , Polynomial transformation for MRI feature extraction, SPIE, pp.1151-1161, 2001.

H. Soltanian-zadeh, J. P. Windham, and D. J. Peck, Optimal linear transformation for MRI feature extraction, Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), pp.74-84, 1996.

H. Soltanian-zadeh, J. P. Windham, and D. J. Peck, Optimal linear transformation for MRI feature extraction, IEEE Transactions on Medical Imaging, vol.15, issue.6, pp.749-767, 1996.
DOI : 10.1109/42.544494

M. Sonka, S. Tadikonda, C. , and S. , Knowledge-based interpretation of MR brain images, IEEE Transactions on Medical Imaging, vol.15, issue.4, pp.443-452, 1996.
DOI : 10.1109/42.511748

D. D. Stark and W. G. Bradley, Magnetic Resonance Imaging, 1999.

R. G. Steen, Edema and tumor perfusion: Characterization by quantitative HMR imaging, American Journal of Radiology, vol.158, pp.259-264, 1992.

C. Studholme, D. Hawkes, and D. Hill, A normalized entropy measure of 3D medical image alignment, Medical Imaging, vol.3338, pp.132-143, 1998.

K. R. Swanson, C. Bridge, J. D. Murray, A. , and E. C. , Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, Journal of the Neurological Sciences, vol.216, issue.1, pp.1-10, 2003.
DOI : 10.1016/j.jns.2003.06.001

T. Woolsey, J. H. Gado, and M. , The Brain Atlas: A Visual Guide to the Human Central Nervous System, 2003.

S. Taheri, S. H. Ong, C. , and V. , Threshold-based 3D Tumor Segmentation using Level Set (TSL), 2007 IEEE Workshop on Applications of Computer Vision (WAC V '07), pp.45-51, 2007.
DOI : 10.1109/WACV.2007.59

M. Taron, N. Paragios, and M. Jolly, Modelling shapes with uncertainties: higher order polynomials, variable bandwidth kernels and non parametric density estimation, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1659-1666, 2005.
DOI : 10.1109/ICCV.2005.153

M. Taron, N. Paragios, and M. Jolly, Uncertainty-Driven Non-parametric Knowledge-Based Segmentation: The Corpus Callosum Case, International Conference on Computer Vision (ICCV) workshop on Variational Geometric and Level Set Methods (VLSM), pp.198-209, 2005.
DOI : 10.1007/11567646_17

D. Terzopoulos, On matching deformable models to images. Topical Meeting on Machine Vision, pp.160-167, 1987.

J. Thirion, Image matching as a diffusion process: an analogy with Maxwell's demons, Medical Image Analysis, vol.2, issue.3, pp.243-260, 1998.
DOI : 10.1016/S1361-8415(98)80022-4

P. Tofts, Quantitative MRI of the brain: Measuring Changes Caused by Disease, 2002.
DOI : 10.1002/0470869526

R. Tolksdorf and E. P. Bontas, Engineering a domain ontology in a semantic web retrieval system for pathology, GI Jahrestagung, pp.569-573, 2004.

K. Tsuchiya, Y. Mizutani, and J. Hachiya, Preliminary evaluation of fluidattenuated inversion-recovery MR in the diagnosis of intracranial tumors, American Journal of Neuroradiology, vol.17, issue.6, pp.1081-1086, 1996.

A. Tuzikov, O. Colliot, and I. Bloch, Evaluation of the symmetry plane in 3D MR brain images, Pattern Recognition Letters, vol.24, issue.14, pp.2219-2233, 2003.
DOI : 10.1016/S0167-8655(03)00049-7

URL : https://hal.archives-ouvertes.fr/hal-01227827

J. K. Udupa, V. R. Leblanc, H. Schmidt, C. Imielinska, P. K. Saha et al., Methodology for evaluating image-segmentation algorithms, Medical Imaging 2002: Image Processing, pp.266-277, 2002.
DOI : 10.1117/12.467166

J. K. Udupa and S. Samarasekera, Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation, Graphical Models and Image Processing, vol.58, issue.3, pp.246-261, 1996.
DOI : 10.1006/gmip.1996.0021

K. Uemura, H. Toyama, S. Baba, Y. Kimura, M. Senda et al., Generation of fractal dimension images and its application to automatic edge detection in brain MRI, Computerized Medical Imaging and Graphics, vol.24, issue.2, pp.73-85, 2000.
DOI : 10.1016/S0895-6111(99)00045-2

V. N. Vapnik, Nature of Statistical Learning Theory, 1999.

L. Verard, P. Allain, J. M. Travere, J. C. Baron, and D. Bloyer, Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis, IEEE Transactions on Medical Imaging, vol.16, issue.5, pp.610-616, 1997.
DOI : 10.1109/42.640751

L. A. Vese and T. F. Chan, A multiphase level set framework for image segmentation using the mumford and shah model, International Journal of Computer Vision, vol.50, issue.3, pp.271-293, 2002.
DOI : 10.1023/A:1020874308076

S. Vinitski, T. Iwanaga, C. Gonzalez, D. G. Andrews, R. Knobler et al., Fast tissue segmentation based on a 4D feature map: Preliminary results, Image Analysis and Processing, 9th International Conference, pp.445-452, 1997.
DOI : 10.1007/3-540-63508-4_154

S. K. Warfield, M. Kaus, F. A. Jolesz, and R. Kikinis, Adaptive, template moderated, spatially varying statistical classification, Medical Image Analysis, vol.4, issue.1, pp.43-55, 2000.
DOI : 10.1016/S1361-8415(00)00003-7

R. Wasserman, R. Acharya, C. Sibata, and K. H. Shin, A data fusion approach to tumor delineation, Proceedings., International Conference on Image Processing, pp.2476-2479, 1995.
DOI : 10.1109/ICIP.1995.537519

S. G. Waxman, Correlative Neuroanatomy, 1999.

P. Y. Wen, S. K. Teoh, and P. M. Black, Brain Tumors, 2001.

R. P. Woods, J. C. Mazziotta, C. , and S. R. , MRI-PET Registration with Automated Algorithm, Journal of Computer Assisted Tomography, vol.17, issue.4, pp.536-546, 1993.
DOI : 10.1097/00004728-199307000-00004

A. Worth, N. Makris, M. Patti, J. Goodman, E. Hoge et al., Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.303-310, 1998.
DOI : 10.1109/42.700743

K. Xie, J. Yang, Z. G. Zhang, and Y. M. Zhu, Semi-automated brain tumor and edema segmentation using MRI, European Journal of Radiology, vol.56, issue.1, pp.12-19, 2005.
DOI : 10.1016/j.ejrad.2005.03.028

URL : https://hal.archives-ouvertes.fr/hal-00443502

C. Xu, D. L. Pham, P. , and J. L. , Handbook of Medical Imaging, Medical Image Segmentation Using Deformable Models: Medical Image Processing and Analysis, pp.129-174, 2000.

C. Xu and J. L. Prince, Snakes, shapes and gradient vector flow, IEEE Transactions on Image Processing, vol.7, issue.3, pp.359-369, 1998.

J. Xue, S. Ruan, B. Moretti, M. Revenu, and D. Bloyet, Knowledge-based segmentation and labeling of brain structures from MRI images, Pattern Recognition Letters, vol.22, issue.3-4, pp.395-405, 2001.
DOI : 10.1016/S0167-8655(00)00135-5

URL : https://hal.archives-ouvertes.fr/hal-00805970

Y. Yang, C. Zheng, L. , and P. , Image thresholding via a modified fuzzy cmeans algorithm, 9th Iberoamerican Congress on Pattern Recognition (CIARP), pp.589-596, 2004.

J. G. Zhang, K. K. Ma, M. H. Er, C. , and V. , Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine, International Workshop on Advanced Image Technology, pp.207-211, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00548532

J. Zhou, K. L. Chan, V. F. Chong, and S. M. Krishnan, Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.6411-6414, 2005.
DOI : 10.1109/IEMBS.2005.1615965

Y. Zhu and H. Yang, Computerized tumor boundary detection using a Hopfield neural network, IEEE Transactions on Medical Imaging, vol.16, issue.1, pp.55-67, 1997.

A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Transactions on Medical Imaging, vol.13, issue.4, pp.716-724, 1994.
DOI : 10.1109/42.363096

A. Zizzari, U. Seiffert, B. Michaelis, G. Gademann, and S. Swiderski, Detection of tumor in digital images of the brain, Proc. of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications SP- PRA 2001, pp.132-137, 2001.

J. M. Zooka, K. M. Iftekharuddin, and . Ahmed, Statistical analysis of fractal-based brain tumor detection algorithms, Magnetic Resonance Imaging, vol.23, issue.5, pp.671-678, 2002.
DOI : 10.1016/j.mri.2005.04.002

T. Tsuchiya, 144 Tofts, Tolksdorf and Bontas Udupa and Samarasekera, pp.72-54, 1996.