H. Abdelmunim, A. Farag, W. Miller, A. , and M. , A kidney segmentation approach from dce-mri using level sets, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1-6, 2008.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua et al., , 2010.

, Slic superpixels

M. Ackerman and S. Ben-david, Discerning linkage-based algorithms among hierarchical clustering methods, IJCAI'11 Proceedings of the TwentySecond international joint conference on Artificial Intelligence, vol.2, pp.1140-1145, 2011.

S. C. Agner, J. Xu, and A. Madabhushi, Spectral embedding based active contour (seac) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging, Medical Physics, issue.3, p.40, 2013.

L. Axel, Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis, Radiology, vol.137, issue.3, pp.679-86, 1980.

Y. Baraud, S. Huet, L. , and B. , Adaptive tests of linear hypotheses by model selection, The Annals of Statistics, vol.31, issue.1, pp.225-251, 2003.

Y. Baraud, S. Huet, L. , and B. , Testing convex hypotheses on the mean of a gaussian vector. application to testing qualitative hypotheses on a regression function, The Annals of Statistics, vol.33, issue.1, pp.214-257, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00756077

J. Baudry, C. Maugis, and B. Michel, Slope heuristics: overview and implementation, Statistics and Computing, vol.22, issue.2, pp.455-470, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00666838

R. L. Berger and J. C. Hsu, Bioequivalence trials, intersection-union tests and equivalence confidence sets, Statistical Science, vol.11, issue.4, pp.283-319, 1996.

J. Besag, Statistical analysis of nonlattice data, Statistician, vol.24, pp.179-195, 1975.

A. Birbrair, T. Zhang, Z. Wang, M. L. Messi, A. Mintz et al., Pericytes at the intersection between tissue regeneration and pathology, Clinical Science, vol.128, issue.2, pp.81-93, 2015.

L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probability Theory and Related Fields, vol.138, pp.33-73, 2007.

G. Brix, Microcirculation and microvasculature in breast tumors, Magnetic Resonance in Medicine, vol.52, pp.420-429, 2004.

C. Brochot, B. Bessoud, D. Balvay, C. Cuénod, N. Siauve et al., Evaluation of antiangiogenic treatment effects on tumors' microcirculation by bayesian physiological pharmacokinetic modeling and magnetic resonance imaging, Magnetic Resonance Imaging, vol.24, issue.8, pp.1059-1067, 2006.
URL : https://hal.archives-ouvertes.fr/inserm-00140800

G. Celeux, F. Forbes, and N. Peyrard, Em procedures using mean field-like approximations for markov model-based image segmentation, Pattern Recognition, vol.36, pp.131-144, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00072526

B. Chalmond, An iterative gibbsian technique for reconstruction of m-ary images, Pattern Recognition, vol.22, issue.6, pp.747-761, 1989.

S. P. Chatzis and T. A. Varvarigou, A fuzzy clustering approach toward hidden markov random field models for enhanced spatially constrained image segmentation, IEEE Transactions on Fuzzy Systems, vol.16, issue.5, pp.1351-1361, 2008.

W. Chen, M. L. Giger, U. Bick, and G. M. Newstead, Automatic identification and classification of characteristic kinetic curves of breast lesions on dce-mri, Medical Physics, vol.33, issue.8, pp.2878-2887, 2006.

B. Chevaillier, D. Mandry, J. Collette, M. Claudon, M. Galloy et al., Functional segmentation of renal dce-mri sequences using vector quantization algorithms, Neural Process Letters, vol.34, issue.1, pp.71-85, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00598278

W. S. Cleveland, E. Grosse, and W. M. Shyu, Local regression models, Statistical Models in S, 1991.

D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.

F. Comte, C. A. Cuenod, M. Pensky, and Y. Rozenholc, Laplace deconvolution on the basis of time domain data and its application to dynamic contrast enhanced imaging, Journal of the Royal Statistical Society, vol.15, issue.3, pp.397-408, 2014.

O. I. Craciunescu, D. S. Yoo, E. Cleland, N. Muradyan, M. D. Carroll et al., Dynamic contrast-enhanced mri in head-and-neck cancer: The impact of region of interest selection on the intra-and interpatient variability of pharmacokinetic parameters, International Journal Radiation Oncology Biology Physics, vol.82, issue.3, pp.345-350, 2012.

C. Durot and Y. Rozenholc, An adaptive test for zero mean, Mathematical Methods of Statistics, vol.15, issue.1, pp.26-60, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00748951

H. P. Eikesdal and R. Kalluri, Drug resistance associated with antiangiogenesis therapy, Seminars in cancer biology, vol.19, 2009.
DOI : 10.1016/j.semcancer.2009.05.006

URL : http://europepmc.org/articles/pmc4001703?pdf=render

E. A. Eisenhauer, P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent et al., New response evaluation criteria in solid tumours: revised recist guideline (version 1.1), European journal of cancer, vol.45, issue.2, pp.228-247, 2009.

J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser et al., Cancer incidence and mortality worldwide: Iarc cancerbase no, 2013.

A. Fieselmann, M. Kowarschik, A. Ganguly, J. Hornegger, and R. Fahrig, Deconvolution-based ct and mr brain perfusion measurement: Theoretical model revisited and practical implementation details, International Journal of Biomedical Imaging, issue.14, pp.1-20, 2011.
DOI : 10.1155/2011/467563

URL : http://downloads.hindawi.com/journals/ijbi/2011/467563.pdf

F. Forbes, N. Peyrard, C. Fraley, D. Georgian-smith, D. M. Goldhaber et al., Model-based region-of-interest selection in dynamic breast mri, Journal of Computer Assisted Tomography, vol.30, pp.675-687, 2006.

E. B. Fowlkes and C. L. Mallows, A method for comparing two hierarchical clusterings, Journal of the American Statistical Association, vol.78, issue.383, pp.553-569, 1983.
DOI : 10.1080/01621459.1983.10478008

B. Glocker, A. Sotiras, N. Komodakis, P. , and N. , Deformable medical image registration: Setting the state of the art with discrete methods, Annual Review of Biomedical Engineering, vol.13, pp.219-244, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00858380

J. Górriza, A. Lassla, J. Ramíreza, D. Salas-gonzaleza, C. Puntonetb et al., Automatic selection of rois in functional imaging using gaussian mixture models, Neuroscience Letters, 2009.

M. J. Graves and D. G. Mitchell, Body mri artifacts in clinical practice: a physicist's and radiologist's perspective, Journal of Magnetic Resonance Imaging, vol.38, issue.2, pp.269-287, 2013.

J. A. Hartigan and M. A. Wong, A k-means clustering algorithm, Applied Statistics, vol.28, pp.100-108, 1979.

R. Hedjam and M. Mignotte, A hierarchical graph-based markovian clustering approach for the unsupervised segmentation of textured color image, IEEE International Conference on Image Processing (ICIP), 2009.

G. T. Herman, Fundamentals of Computerized Tomography: Image Reconstruction from Projections, 2009.

T. Heye, E. M. Merkle, C. S. Reiner, M. S. Davenport, J. J. Horvath et al., Reproducibility of dynamic contrast-enhanced mr imaging part ii. comparison of intra-and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis, Radiology, vol.266, issue.3, pp.812-821, 2013.

C. Hsu and C. Lin, A comparison on methods for multi-class support vector machines, IEEE Transactions on Neural Networks, vol.13, pp.415-425, 2002.

H. Ichihashi, K. Miyagishi, and K. Honda, Fuzzy c-means clustering with regularization by k-l information, 10th IEEE International Conference on Fuzzy System, pp.924-927, 2001.

N. Iftimia, W. R. Brugge, and D. X. Hammer, Advances in Optical Imaging for Clinical Medicine, 2011.

M. Ingrisch and S. Sourbron, Tracer-kinetic modeling of dynamic contrastenhanced mri and ct: a primer, Journal of Pharmacokinetics and Pharmacodynamics, vol.40, issue.3, pp.281-300, 2013.

B. Irving, A. Cifor, B. W. Papie?, J. Franklin, E. M. Anderson et al., Automated colorectal tumour segmentation in dcemri using supervoxel neighbourhood contrast characteristics, In Medical Image Computing and Computer-Assisted Intervention -MICCAI, vol.8673, pp.609-616, 2014.

B. Irving, J. M. Franklin, B. W. Papie?, E. M. Anderson, R. A. Sharma et al., Pieces-of-parts for supervoxel segmentation with global context: Application to dce-mri tumour delineation, Medical Image Analysis, vol.32, pp.69-83, 2016.

R. Jain, Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy, Science, vol.307, issue.5706, pp.58-62, 2005.

N. Kachenoura, P. Cluzel, F. Frouin, D. Toledano, P. Grenier et al., Evaluation of an edge-based registration method: application to magnetic resonance first-pass myocardial perfusion data, Magnetic Resonance Imaging, vol.29, issue.6, pp.853-860, 2011.

S. Koscielny, M. Tubiana, M. G. Lê, J. Valleron, H. Mouriesse et al., Breast cancer: relationship between the size of the primary tumour and the probability of metastatic dissemination, British journal of cancer, vol.49, issue.6, pp.709-715, 1984.

V. Kulasingam and E. P. Diamandis, Strategies for discovering novel cancer biomark-ers through utilization of emerging technologies, Nature clinical practice Oncology, vol.5, issue.10, pp.588-599, 2008.

C. Lavini, M. C. De-jonge, M. G. Van-de-sande, P. P. Tak, A. J. Nederveen et al., Pixel-by-pixel analysis of dce mri curve patterns and an illustration of its application to the imaging of the musculoskeletal system, Magnetic Resonance Imaging, vol.25, pp.604-612, 2006.

J. Lecoeur, J. Ferre, D. L. Collins, S. P. Morrissey, and C. Barillot, , 2009.

, Multi channel mri segmentation with graph cuts using spectral gradient and multidimensional gaussian mixture model, SPIE-Medical Imaging, vol.7259, pp.72593-72594

S. Li, F. G. Zöllner, A. D. Merrem, Y. Peng, J. Roervik et al., Wavelet-based segmentation of renal compartments in dce-mri of human kidney: Initial results in patients and healthy volunteers, Computerized Medical Imaging and Graphics, vol.36, pp.108-118, 2012.

S. Li, F. G. Zöllner, A. D. Merrem, Y. Peng, J. Roervik et al., Wavelet-based segmentation of renal compartments in dce-mri of human kidney: Initial results in patients and healthy volunteers, Computerized Medical Imaging and Graphics, vol.36, pp.108-118, 2012.

H. Liu, Y. Liu, Z. Zhao, L. Zhang, and T. Qiu, A new background distribution-based active contour model for three-dimensional lesion segmentation in breast dce-mri, Medical Physics, p.82303, 2014.

J. A. Ludwig and J. N. Weinstein, Biomarkers in cancer staging, prognosis and treatment selection, Nature reviews Cancer, vol.5, issue.11, pp.845-856, 2005.

J. Maroquin, S. Mitte, and T. Poggio, Probabilistic solution of ill-posed problems in computational vision, Journal of the American Statistical Association, vol.82, pp.76-89, 1987.

P. Massart, Concentration Inequalities and Model Selection, 2003.

D. Mcclymont, A. Mehnert, A. Trakic, D. Kennedy, and S. Crozier, Fully automatic lesion segmentation in breast mri using mean-shift and graph-cuts on a region adjacency graph, Journal of Magnetic Resonance Imaging, vol.39, pp.795-804, 2014.

G. Mclachlan and D. Peel, Finite Mixture Models. Probability and Statistics Series, 2000.

M. Medved, M. Medved, G. Karczmar, C. Yang, J. Dignam et al., Semiquantitative analysis of dynamic contrast enhanced mri in cancer patients: variability and changes in tumor tissue over time, Magnetic Resonance Imaging, vol.20, pp.122-128, 2004.

S. Miyamoto and M. Mukaidono, Fuzzy c-means as a regularization and maximum entropy approach, Proceedings of 7th International Fuzzy System Association World Congress, vol.2, pp.86-92, 1997.

A. Ortiz, J. M. Górriz, J. Ramírez, and F. J. Martinez-murcia, Automatic roi selection in structural brain mri using som 3d projection, PLoS ONE, vol.9, issue.4, p.93851, 2014.

L. Ostergaard, A. G. Sorensen, K. K. Kwong, R. M. Weisskoff, C. Gyldensted et al., High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. part ii: Experimental comparison and preliminary results, Magnetic Resonance in Medicine, vol.36, issue.5, pp.726-762, 1996.

N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-66, 1979.

B. D. Ripley, Pattern Recognition and Neural Networks, 1996.

V. J. Schmid, B. Whitcher, A. R. Padhani, Y. , and G. , Quantitative analysis of dynamic contrast-enhanced mr images based on bayesian, pp.p-splines, 2009.

, IEEE Transactions on Medical Imaging, vol.28, issue.6, pp.789-798

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.888-905, 2000.

J. Shi, B. Sahiner, H. Chan, C. Paramagul, L. M. Hadjiiski et al., Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation, Medical Physics, vol.36, pp.5052-5063, 2009.

H. Shou, R. T. Shinohara, H. Liu, D. S. Reich, and C. M. Crainiceanu, Soft null hypotheses: A case study of image enhancement detection in brain lesions, Journal of Computational and Graphical Statistics, vol.25, issue.2, pp.570-588, 2016.

A. Sotiras, N. Komodakis, B. Glocker, J. Deux, P. et al., , 2009.

, Graphical models and deformable diffeomorphic population registration using global and local metrics, MICCAI, pp.672-679

J. Stanley, W. Shipley, and G. Steel, Influence of tumour size on hypoxic fraction and therapeutic sensitivity of lewis lung tumour, British journal of cancer, 1977.

M. J. Stoutjesdijk, J. Veltman, H. Huisman, N. Karssemeijer, J. O. Barentsz et al., Automated analysis of contrast enhancement in breast mri lesions using mean shift clustering for roi selection, Journal of Magnetic Resonance Imaging, vol.26, pp.606-614, 2007.

M. J. Stoutjesdijk, M. Zijp, C. Boetes, N. Karssemeijer, J. O. Barentsz et al., Computer aided analysis of breast mri enhancement kinetics using mean shift clustering and multifeature iterative region of interest selection, Journal of Magnetic Resonance Imaging, vol.36, pp.1104-1112, 2012.

A. A. Taha and A. Hanbury, Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool, BMC Medical Imaging, 2015.

W. Tao, H. Jin, and Y. Zhang, Color image segmentation based on mean shift and normalized cuts, IEEE Transactions on Systems, Man, and Cybernetics, vol.37, issue.5, pp.1382-1389, 2007.
DOI : 10.1109/tsmcb.2007.902249

G. Tartare, D. Hamad, M. Azahaf, P. Puech, and N. Betrouni, Spectral clustering applied for dynamic contrast-enhanced mr analysis of time-intensity curves, Computerized Medical Imaging and Graphics, vol.38, issue.8, pp.702-713, 2014.

I. Thomassin-naggara, D. Balvay, E. Aubert, E. Daraï, R. Rouzier et al., Quantitative dynamic contrast-enhanced mr imaging analysis of complex adnexal masses: a preliminary study, European Radiology, vol.22, issue.4, pp.738-745, 2012.

I. Thomassin-naggara, M. Bazot, E. Daraí, P. Callard, J. Thomassin et al., Epithelial ovarian tumors: value of dynamic contrastenhanced mr imaging and correlation with tumor angiogenesis, Radiology, vol.248, pp.148-159, 2008.

I. Thomassin-naggara, N. Soualhi, D. Balvay, and C. A. Cuenod,

, Quantifying tumour vascular heterogeneity with dce-mri in complex adnexal masses, Journal of Magnetic Resonance Imaging

I. Thomassin-naggara, I. Toussaint, N. Perrot, R. Rouzier, C. Cuenod et al., Characterization of complex adnexal masses: value of adding perfusion-and diffusion-weighted mr imaging to conventional mr imaging, Radiology, vol.258, issue.3, pp.793-803, 2011.

J. C. Tilton, Y. Tarabalka, P. M. Montesano, and E. Gofman, Best merge region-growing segmentation with integrated nonadjacent region object aggregation, IEEE Transactions on Geoscience and Remote Sensing, vol.11, pp.4454-4467, 2012.
DOI : 10.1109/tgrs.2012.2190079

P. S. Tofts, Modeling tracer kinetics in dynamic gd-dtpa mr imaging, Journal of Magnetic Resonance Imaging, vol.7, pp.91-101, 1997.
DOI : 10.1002/jmri.1880070113

P. S. Tofts, B. Berkowitz, and M. D. Schnall, Quantitative analysis of dynamic gd-dtpa enhancement in breast tumors using a permeability model, Magnetic Resonance in Medicine, vol.33, pp.564-568, 1995.

A. Tremeau and N. Borel, A region growing and merging algorithm to color segmentation, Pattern Recognition, vol.30, issue.7, pp.1191-1203, 1997.

Z. Tu and S. Zhu, Image segmentation by data-driven markov chain monte carlo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.657-673, 2002.

N. Tuncbilek, H. M. Karakas, A. , and S. , Dynamic mri in indirect estimation of microvessel density, histologic grade, and prognosis in colorectal adenocarcinomas, Abdom Imaging, vol.29, pp.166-72, 2004.

N. Tuncbilek, H. M. Karakas, and O. O. Okten, Dynamic contrast enhanced mri in the differential diagnosis of soft tissue tumors, Europeen Journal Radiology, vol.53, pp.500-505, 2005.

A. Vedaldi and S. Soatto, Quick shift and kernel methods for mode seeking, European Conference on Computer Vision (ECCV), 2008.
DOI : 10.1007/978-3-540-88693-8_52

URL : http://vision.ucla.edu/papers/vedaldiS08quick.pdf

E. Walker and A. S. Nowacki, Understanding equivalence and noninferiority testing, Journal of General Internal Medicine, vol.26, issue.2, pp.192-196, 2010.
DOI : 10.1007/s11606-010-1513-8

URL : http://europepmc.org/articles/pmc3019319?pdf=render

S. Wellek, Testing Statistical Hypotheses of Equivalence and Noninferiority, 2010.
DOI : 10.1201/ebk1439808184

Q. Wu, M. Salganicoff, A. Krishnan, D. S. Fussell, and M. K. Markey, , 2006.

, Interactive lesion segmentation on dynamic contrast enhanced breast mri using a markov model, Image Processing, issue.1, p.6144, 2006.

L. Zelnik-manor and P. Perona, Self-tuning spectral clustering, Advances in Neural Information Processing Systems, vol.17, pp.1601-1608, 2004.

F. G. Zöllner, R. Sance, P. Rogelj, M. J. Ledesma-carbayo, J. Rørvik et al., Assessment of 3d dce-mri of the kidneys using nonrigid image registration and segmentation of voxel time courses, Computerized Medical Imaging and Graphics, vol.33, pp.171-181, 2009.