Fully automatic segmentation of white matter hyperintensities in MR images of the elderly, NeuroImage, vol.28, issue.3, pp.607-617, 2005. ,
DOI : 10.1016/j.neuroimage.2005.06.061
An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.1122-1129, 2006. ,
DOI : 10.1109/CVPR.2006.55
Shape Quantization and Recognition with Randomized Trees, Neural Computation, vol.1, issue.1, pp.1545-1588, 1997. ,
DOI : 10.1016/0031-3203(90)90098-6
Probabilistic segmentation of white matter lesions in MR imaging, Probabilistic Segmentation of White Matter Lesions in MR Imaging, pp.1037-1044, 2004. ,
DOI : 10.1016/j.neuroimage.2003.10.012
Automated MS-lesion segmentation by K-Nearest neighbor classification, The MIDAS Journal -MS Lesion Segmentation (MICCAI 2008 Workshop), 2008. ,
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification, DAGM-Symposium, pp.142-152, 2008. ,
DOI : 10.1007/978-3-540-69321-5_15
Advances in Kernel Methods: Support Vector Learning, 1999. ,
Pattern Recognition and Machine Learning, 2006. ,
Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001. ,
DOI : 10.1023/A:1010933404324
Classification and Regression Trees, 1984. ,
Ms lesion segmentation based on hidden markov chains In: 11 th International conference on medical image computing and computer assisted intervention. Paper selected for ?a grand challenge : 3D segmentation in the clinic, 2008. ,
Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains, Medical Image Analysis, vol.12, issue.6, pp.639-652, 2008. ,
DOI : 10.1016/j.media.2008.03.001
Decision forests with longrange spatial context for organ localization in CT volumes, MICCAI workshop on Probabilistic Models for Medical Image Analysis (MICCAI- PMMIA), 2009. ,
Regression Forests for Efficient Anatomy Detection and Localization in CT Studies, MICCAI workshop on Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging (MICCAI-MCV), 2010. ,
DOI : 10.1007/978-3-642-18421-5_11
Segmentation and quantification of black holes in multiple sclerosis, NeuroImage, vol.29, issue.2, pp.467-474, 2006. ,
DOI : 10.1016/j.neuroimage.2005.07.042
Hierarchical segmentation of multiple sclerosis lesions in multi-sequence MRI, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.157-160, 2004. ,
DOI : 10.1109/ISBI.2004.1398498
URL : https://hal.archives-ouvertes.fr/inria-00615969
Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution, International Journal of Biomedical Imaging, vol.5, issue.1, pp.1-13, 2009. ,
DOI : 10.1007/3-540-48236-9_13
Grey matter pathology in multiple sclerosis, The Lancet Neurology, vol.7, issue.9, pp.841-851, 2008. ,
DOI : 10.1016/S1474-4422(08)70191-1
Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography, FIMH. LNCS 5528, pp.447-456, 2009. ,
DOI : 10.1109/TMI.2007.906089
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinformatics, vol.10, issue.1, p.213, 2009. ,
DOI : 10.1186/1471-2105-10-213
Maximum Likelihood Estimation of the Bias Field in MR Brain Images: Investigating Different Modelings of the Imaging Process, MICCAI. LNCS 2208, pp.811-819, 2001. ,
DOI : 10.1007/3-540-45468-3_97
URL : https://hal.archives-ouvertes.fr/inria-00615873
Computation of the Mid-Sagittal Plane in 3D Images of the Brain, IEEE Trans. Med. Imaging, vol.21, issue.2, pp.122-138, 2002. ,
DOI : 10.1007/3-540-45053-X_44
URL : https://hal.archives-ouvertes.fr/inria-00615857
C4.5: Programs for Machine Learning, 1993. ,
Détection et quantification de processusévolutifsprocessusévolutifs dans des images médicales tridimensionnelles : applicationàapplicationà la sclérose en plaques, Thèse de sciences, 2002. ,
Multiple sclerosis lesion segmentation using statistical and topological atlases, The MIDAS Journal -MS Lesion Segmentation (MICCAI 2008 Workshop), 2008. ,
A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions, NeuroImage, vol.49, issue.2, pp.1524-1535, 2010. ,
DOI : 10.1016/j.neuroimage.2009.09.005
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, International Journal of Computer Vision, vol.62, issue.1???2, pp.2-23, 2009. ,
DOI : 10.1007/s11263-007-0109-1
An automatic segmentation of T2-FLAIR multiple sclerosis lesions, The MIDAS Journal -MS Lesion Segmentation (MICCAI 2008 Workshop), 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00616119
3D segmentation in the clinic: A grand challenge II: MS lesion segmentation, In: MIDAS Journal. pp, pp.1-5, 2008. ,
MS lesion segmentation challenge, 2008. ,
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
Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI, NeuroImage, vol.32, issue.3, pp.1205-1215, 2006. ,
DOI : 10.1016/j.neuroimage.2006.04.211
Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine, Computerized Medical Imaging and Graphics, vol.34, issue.5, pp.404-413, 2010. ,
DOI : 10.1016/j.compmedimag.2010.02.001
Discriminative, Semantic Segmentation of Brain Tissue in MR Images, LNCS, vol.5762, pp.558-565, 2009. ,
DOI : 10.1007/978-3-642-04271-3_68
Bilayer segmentation of webcam videos using tree-based classifiers, Trans. Pattern Analysis and Machine Intelligence (PAMI), vol.33, 2010. ,