Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical conditional random fields for detection of gad-enhancing lesions in multiple sclerosis
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Accurate detection and delineation of myocardium infarction is important for treatment planning in patients with heart disease. Delayed contrast enhanced magnetic resonance imaging (DE-MRI) is a well established technique for the assessment of myocardial infarction. However, manual delineation of myocardium infarction in DE-MRI is both time consuming and prone to intra and inter rater variability. In this paper, we present an automatic, probabilistic framework for segmentation of myocardium infarction using Hierarchical Conditional Random Fields (HCRFs). In each level, a CRF classifier with up to triplet clique potentials is learnt. Furthermore, incorporation of spin image features in the second level allows for better learning the neighbourhood characteristics. The performance of the HCRF classifier on 5 animal scans and 5 human scans shows promising results.