An Immunological Model of Distributed Detection and Its Application to Computer Security, 1999. ,
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. ,
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
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
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
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
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
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
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
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
Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, pp.1-38, 1977. ,
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
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
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
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
Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK -The Insight Toolkit," roc. of Medicine Meets Virtual Reality, 2002. ,
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. ,
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. ,
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. ,
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
Random Forests, Machine Learning, 2001. ,
Decision Forests for Computer Vision and Medical Image Analysis, 2013. ,
DOI : 10.1007/978-1-4471-4929-3
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
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
On Discriminative vs Generative classifiers: A comparison of logistic regression and naive Bayes, NIPS 2001, 2001. ,
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. ,
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. ,
Multi-fractal texture estimation for detection and segmentation of brain tumors, IEEE Transactions on Biomedical Engineering, 2013. ,
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
Random forests-classification description, 2004. ,
N4ITK: Nick's N3 ITK implementation for MRI bias field correction, The Insight Journal, 2010. ,
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
Context-sensitive classification forests for segmentation of brain tumor tissues, Proceedings MICCAI-BRATS, pp.1-9, 2012. ,
Efficacy of texture , shape and intensity features for robust posterior-fossa tumor segmentation in MRI, SPIE Med. Imag, vol.7260, 2009. ,
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
Fractal analysis of tumor in brain MR images, Machine Vision and Applications, pp.352-362, 2003. ,
DOI : 10.1007/s00138-002-0087-9
Random forest Statistics department, 2001. ,
Classification and regression by random forests, 2002. ,
Classication and regression tree methods, Encyclopedia of Statistics in Quality and Reliability, 2008. ,
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
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
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-#
Response criteria for glioma, Nature Clinical Practice Oncology, vol.19, issue.11, pp.634-644, 2008. ,
DOI : 10.1038/ncponc1204
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
Automated Segmentation of MR Images of Brain Tumors, Radiology, vol.218, issue.2, pp.586-591, 2001. ,
DOI : 10.1148/radiology.218.2.r01fe44586
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
MRI segmentation: Methods and applications, Magnetic Resonance Imaging, vol.13, issue.3, pp.323-334, 1995. ,
DOI : 10.1016/0730-725X(94)00124-L
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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