Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
International Journal of Computer Vision
Automated segmentation of the menisci from MR images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Primal/dual linear programming and statistical atlases for cartilage segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Segmenting brain tumors with conditional random fields and support vector machines
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Machine learning in medical imaging
Machine Vision and Applications
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Segmenting articular cartilage and meniscus from magnetic resonance (MR) images is an essential task for the assessment of knee pathology. Most of the previous classification-based works for cartilage and meniscus segmentation only rely on independent labellings by a classifier, but do not consider the spatial context interaction. The labels of most image voxels are actually dependent upon their neighbours. In this study, we present an automatic knee segmentation system working on multi-contrast MR images where a novel classification model unifying an extreme learning machine (ELM)-based association potential and a discriminative random field (DRF)-based interaction potential is proposed. The DRF model introduces spatial dependencies between neighbouring voxels to the independent ELM classification. We exploit a rich set of features From multi-contrast MR images to train the proposed classification model and perform the loopy belief propagation for the inference. The proposed model is evaluated on multi-contrast MR datasets acquired from 11 subjects with results outperforming the independent classifiers in terms of segmentation accuracy of both cartilages and menisci.