Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
暂无分享,去创建一个
D. Louis Collins | Hassan Rivaz | Tal Arbel | Zahra Karimaghaloo | Douglas L. Arnold | D. Collins | T. Arbel | D. Arnold | H. Rivaz | Zahra Karimaghaloo
[1] D. Louis Collins,et al. Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields , 2012, IEEE Transactions on Medical Imaging.
[2] Mark W. Schmidt,et al. Structure learning in random fields for heart motion abnormality detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[3] Cordelia Schmid,et al. A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] B J Bedell,et al. Automatic segmentation of gadolinium‐enhanced multiple sclerosis lesions , 1998, Magnetic resonance in medicine.
[5] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[6] Pushmeet Kohli,et al. Associative hierarchical CRFs for object class image segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[7] Martial Hebert,et al. Discriminative Random Fields , 2006, International Journal of Computer Vision.
[8] D. Louis Collins,et al. Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields , 2010, MICCAI.
[9] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[10] Alan C. Evans,et al. A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.
[11] Haibin Ling,et al. An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Ponnada A Narayana,et al. Automatic delineation of Gd enhancements on magnetic resonance images in multiple sclerosis. , 2002, Medical physics.
[13] Joost van de Weijer,et al. Harmony Potentials , 2011, International Journal of Computer Vision.
[14] D. Louis Collins,et al. Adaptive Voxel, Texture and Temporal Conditional Random Fields for Detection of Gad-Enhancing Multiple Sclerosis Lesions in Brain MRI , 2013, MICCAI.
[15] Koenraad Van Leemput,et al. Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.
[16] D. Louis Collins,et al. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.
[17] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[18] Cordelia Schmid,et al. Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).
[19] Luca Roccatagliata,et al. Magnetic resonance imaging as a potential surrogate for relapses in multiple sclerosis: A meta‐analytic approach , 2009, Annals of neurology.
[20] Philip H. S. Torr,et al. What, Where and How Many? Combining Object Detectors and CRFs , 2010, ECCV.
[21] Dominik S. Meier,et al. Time-series modeling of multiple sclerosis disease activity: A promising window on disease progression and repair potential? , 2007, Neurotherapeutics.
[22] Mark W. Schmidt,et al. Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines , 2005, CVBIA.
[23] Koen L. Vincken,et al. Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.
[24] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[25] J K Udupa,et al. Computer-assisted quantitation of enhancing lesions in multiple sclerosis: correlation with clinical classification. , 1997, AJNR. American journal of neuroradiology.
[26] F. Jolesz,et al. MRI contrast uptake in new lesions in relapsing-remitting MS followed at weekly intervals , 2003, Neurology.
[27] D. Louis Collins,et al. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging , 2013, Medical Image Anal..
[28] D. Louis Collins,et al. Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.
[29] Massimo Filippi,et al. An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[30] D. Louis Collins,et al. Hierarchical Conditional Random Fields for Detection of Gad-Enhancing Lesions in Multiple Sclerosis , 2012, MICCAI.
[31] Stefan Bauer,et al. Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization , 2011, MICCAI.
[32] Qiang Wang,et al. Combining CRF and Multi-hypothesis Detection for Accurate Lesion Segmentation in Breast Sonograms , 2012, MICCAI.
[33] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[34] Jayaram K. Udupa,et al. New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.
[35] D. Arnold,et al. B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. , 2008, The New England journal of medicine.
[36] D. Louis Collins,et al. Temporally Consistent Probabilistic Detection of New Multiple Sclerosis Lesions in Brain MRI , 2013, IEEE Transactions on Medical Imaging.
[37] D. Louis Collins,et al. Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .
[38] Ponnada A Narayana,et al. Segmentation of gadolinium‐enhanced lesions on MRI in multiple sclerosis , 2007, Journal of magnetic resonance imaging : JMRI.