S. A. Hofmeyr, An Immunological Model of Distributed Detection and Its Application to Computer Security, 1999.

M. Oprea and S. Forrest, How the immune system generates diversity: Pathogen space coverage with random and evolved antibody libraries, GECCO 99, Real-world Applications Track, pp.1651-1656, 1999.

A. A. Younis, A. T. Soliman, M. R. Kabuka, J. , and N. M. , MS Lesions Detection in MRI Using Grouping Artificial Immune Networks, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, pp.1139-1146, 2007.
DOI : 10.1109/BIBE.2007.4375704

K. H. Zou, S. K. Warfield, A. Bharatha, C. M. Tempany, M. R. Kaus et al., Statistical validation of image segmentation quality based on a spatial overlap index1, Academic Radiology, vol.11, issue.2, pp.178-189, 2004.
DOI : 10.1016/S1076-6332(03)00671-8

B. A. Abdullah, A. A. Younis, and N. M. John, Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs, The Open Biomedical Engineering Journal, vol.6, issue.1, pp.56-72, 2012.
DOI : 10.2174/1874120701206010056

B. A. Abdullah, Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs, Open Journal of Medical Imaging, vol.01, issue.02, pp.26-42, 2011.
DOI : 10.4236/ojmi.2011.12005

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR, Med. Image Comput. Comput.-Assist. Interv. Miccai Int. Conf. Med. Image Comput. Comput.-Assist. Interv, vol.15, pp.369-376, 2012.
DOI : 10.1007/978-3-642-33454-2_46

S. Bauer, L. Nolte, and M. Reyes, Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization, Med. Image Comput, vol.15, issue.8
DOI : 10.1007/978-3-540-85988-8_9

F. Rousseau, P. A. Habas, and C. Studholme, A Supervised Patch-Based Approach for Human Brain Labeling, IEEE Transactions on Medical Imaging, vol.30, issue.10, pp.1852-1862, 2011.
DOI : 10.1109/TMI.2011.2156806

URL : https://hal.archives-ouvertes.fr/hal-00631458

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2012, pp.369-376, 2012.
DOI : 10.1007/978-3-642-33454-2_46

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

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4

URL : https://hal.archives-ouvertes.fr/inria-00072526

B. Scherrer, F. Forbes, C. Garbay, and M. Dojat, Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation, In: MICCAI, pp.1066-1074, 2008.
DOI : 10.1007/978-3-540-85990-1_128

URL : https://hal.archives-ouvertes.fr/inserm-00356883

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, vol.29, issue.6, pp.1310-1320, 2010.
DOI : 10.1109/TMI.2010.2046908

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3071855

L. G. Nyul, J. K. Udupa, and X. Zhang, New variants of a method of MRI scale standardization, IEEE Transactions on Medical Imaging, vol.19, issue.2, pp.143-150, 2000.
DOI : 10.1109/42.836373

T. S. Yoo, M. J. Ackerman, W. E. Lorensen, W. Schroeder, V. Chalana et al., Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK -The Insight Toolkit," roc. of Medicine Meets Virtual Reality, 2002.

A. Criminisi, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, Foundations and Trends® in Computer Graphics and Vision, 2011.

K. Laws, S. Bauer, T. Fejes, J. Slotboom, R. Wiest et al., Rapid Texture Identification Segmentation of Brain Tumor Images Based on Integrated Hierarchical Classification and Regularization, Proc. SPIE 0238, Image Processing for Missile Guidance Proceedings of MICCAI-BRATS 2012, 1980.

S. Bauer, L. Nolte, and M. Reyes, Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization. Medical image computing and computer-assisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011.

S. Bauer, R. Wiest, L. Nolte, and M. Reyes, A survey of MRI-based medical image analysis for brain tumor studies, Physics in Medicine and Biology, vol.58, issue.13, p.58, 2013.
DOI : 10.1088/0031-9155/58/13/R97

L. Breiman, Random Forests, Machine Learning, 2001.

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, 2013.
DOI : 10.1007/978-1-4471-4929-3

E. Geremia, D. Zikic, O. Clatz, B. H. Menze, E. Glocker et al., Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI, Decision Forests for Computer Vision and Medical Image Analysis, pp.245-260, 2013.
DOI : 10.1007/978-1-4471-4929-3_17

URL : https://hal.archives-ouvertes.fr/hal-00931809

N. Komodakis, G. Tziritas, and N. Paragios, Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies, Computer Vision and Image Understanding, vol.112, issue.1, 2008.
DOI : 10.1016/j.cviu.2008.06.007

URL : https://hal.archives-ouvertes.fr/hal-00918699

A. Y. Ng and M. I. Jordan, On Discriminative vs Generative classifiers: A comparison of logistic regression and naive Bayes, NIPS 2001, 2001.

Z. Tu and X. Bai, Auto-context and Its Application to High-level Vision Tasks and 3D Brain Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.10, p.32, 2009.

D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp et al., Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. Medical image computing and computer-assisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012.

A. Islam, S. Reza, and K. M. Iftekharuddin, Multi-fractal texture estimation for detection and segmentation of brain tumors, IEEE Transactions on Biomedical Engineering, 2013.

S. Ahmed, K. Iftekharuddin, and A. Vossough, Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI, IEEE Transactions on Information Technology in Biomedicine, vol.15, issue.2, pp.206-213, 2011.
DOI : 10.1109/TITB.2011.2104376

A. Cutler and L. Breiman, Random forests-classification description, 2004.

N. Tustison and J. Gee, N4ITK: Nick's N3 ITK implementation for MRI bias field correction, The Insight Journal, 2010.

L. G. Nyul, J. K. Udupa, and X. Zhang, New variants of a method of MRI scale standardization, IEEE Transactions on Medical Imaging, vol.19, issue.2, pp.143-150, 2000.
DOI : 10.1109/42.836373

D. Zikic, B. Glocker, E. Konkoglu, J. Shotton, A. Criminisi et al., Context-sensitive classification forests for segmentation of brain tumor tissues, Proceedings MICCAI-BRATS, pp.1-9, 2012.

K. M. Iftekharuddin, S. Ahmed, R. J. Ogg, and F. H. Laningham, Efficacy of texture , shape and intensity features for robust posterior-fossa tumor segmentation in MRI, SPIE Med. Imag, vol.7260, 2009.

T. Leung and J. Malik, Representing and recognizing the visual appearance of materials using three-dimensional textons, International Journal of Computer Vision, vol.43, issue.1, pp.29-44, 2001.
DOI : 10.1023/A:1011126920638

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

L. Breiman, Random forest Statistics department, 2001.

M. Wiener and A. Liaw, Classification and regression by random forests, 2002.

W. Loh, Classication and regression tree methods, Encyclopedia of Statistics in Quality and Reliability, 2008.

A. Criminisi, J. Shotton, and E. Konukoglu, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, Foundations and Trends?? in Computer Graphics and Vision, vol.7, issue.2-3, p.2012
DOI : 10.1561/0600000035

J. Corso, E. Sharon, S. Dube, S. El-saden, U. Sinha et al., Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification, IEEE Transactions on Medical Imaging, vol.27, issue.5, pp.629-640, 2008.
DOI : 10.1109/TMI.2007.912817

B. Alfano, A. Brunetti, M. Larobina, M. Quarantelli, E. Tedeschi et al., Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis, Journal of Magnetic Resonance Imaging, vol.9, issue.6, pp.799-807, 2000.
DOI : 10.1002/1522-2586(200012)12:6<799::AID-JMRI2>3.0.CO;2-#

A. Sorensen, Response criteria for glioma, Nature Clinical Practice Oncology, vol.19, issue.11, pp.634-644, 2008.
DOI : 10.1038/ncponc1204

M. Vaidyanathan, Monitoring brain tumor response to therapy using MRI segmentation, Magnetic Resonance Imaging, vol.15, issue.3, pp.323-334, 1997.
DOI : 10.1016/S0730-725X(96)00386-4

M. Kaus, S. Warfield, A. Nabavi, P. Black, F. 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. Vos, Interobserver variability in the radiological assessment of response to chemotherapy in glioma, Neurology, vol.60, issue.5, pp.826-830, 2003.
DOI : 10.1212/01.WNL.0000049467.54667.92

L. Clarke, MRI segmentation: Methods and applications, Magnetic Resonance Imaging, vol.13, issue.3, pp.323-334, 1995.
DOI : 10.1016/0730-725X(94)00124-L

B. Joe, Brain Tumor Volume Measurement: Comparison of Manual and Semiautomated Methods, Radiology, vol.212, issue.3, pp.811-816, 1999.
DOI : 10.1148/radiology.212.3.r99se22811

E. Zijlstra, Radiotherapy response of cerebral metastases quantified by serial MR imaging, Journal of Neuro-Oncology, vol.26, issue.2, pp.171-176, 1994.
DOI : 10.1007/BF01052901

R. Dubey, Evaluation of Three Methods for MRI Brain Tumor Segmentation, 2011 Eighth International Conference on Information Technology: New Generations, 2011.
DOI : 10.1109/ITNG.2011.92

J. Corso, E. Sharon, S. Dube, S. El-saden, U. Sinha et al., Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification, IEEE Transactions on Medical Imaging, vol.27, issue.5, pp.629-640, 2008.
DOI : 10.1109/TMI.2007.912817

M. Kumar, Novel membrane-permeable contrast agent for brain tumor detection by MRI, Magnetic Resonance in Medicine, vol.53, issue.3, pp.617-624, 2010.
DOI : 10.1002/mrm.22216

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein et al., A reproducible evaluation of ANTs similarity metric performance in brain image registration, NeuroImage, vol.54, issue.3, pp.2033-2077, 2011.
DOI : 10.1016/j.neuroimage.2010.09.025

B. B. Avants, N. J. Tustison, J. Wu, P. A. Cook, and J. C. Gee, An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data, Neuroinformatics, vol.20, issue.Suppl 1, pp.381-400, 2011.
DOI : 10.1007/s12021-011-9109-y

B. B. Avants, P. Yushkevich, J. Pluta, D. Minkoff, M. Korczykowski et al., The optimal template effect in hippocampus studies of diseased populations, NeuroImage, vol.49, issue.3, pp.2457-66, 2010.
DOI : 10.1016/j.neuroimage.2009.09.062

B. A. Landman, A. J. Huang, A. Gifford, D. S. Vikram, I. A. Lim et al., Multi-parametric neuroimaging reproducibility: A 3-T resource study, NeuroImage, vol.54, issue.4, pp.2854-66, 2011.
DOI : 10.1016/j.neuroimage.2010.11.047

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, vol.29, issue.6, pp.1310-1330, 2010.
DOI : 10.1109/TMI.2010.2046908

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3071855

R. Achanta, A. Shaji, K. Smith, P. Lucchi, S. Fua et al., SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.
DOI : 10.1109/TPAMI.2012.120

Y. Boykov, O. Veksler, and R. Zabih, Efficient approximate energy minimization via graph cuts, IEEE TPAMI, vol.20, issue.12, pp.1222-1239, 2001.
DOI : 10.1109/iccv.1999.791245

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

J. J. Corso, E. Sharon, S. Dube, S. El-saden, U. Sinha et al., Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification, IEEE Transactions on Medical Imaging, vol.27, issue.5, pp.629-640, 2008.
DOI : 10.1109/TMI.2007.912817

S. Gould, Multiclass pixel labeling with non-local matching constraints, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2783-2790, 2012.
DOI : 10.1109/CVPR.2012.6248002

D. Kroon and K. Slump, MRI modalitiy transformation in demon registration, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.963-966, 2009.
DOI : 10.1109/ISBI.2009.5193214

S. M. Smith and J. M. Brady, Susan -a new approach to low level image processing, International Journal of Computer Vision, vol.23, issue.1, pp.45-78, 1997.
DOI : 10.1023/A:1007963824710