Robust Real-Time Face Detection
International Journal of Computer Vision
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images
International Journal of Computer Vision
Automated, accurate and fast segmentation of 4D cardiac MR images
FIMH'07 Proceedings of the 4th international conference on Functional imaging and modeling of the heart
Cardiac anchoring in MRI through context modeling
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Laplacian eigenmaps manifold learning for landmark localization in brain MR images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Accurate regression-based 4D mitral valve surface reconstruction from 2D+t MRI slices
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Landmark detection in cardiac MRI using learned local image statistics
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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Cardiac magnetic resonance (MR) imaging has advanced to become a powerful diagnostic tool in clinical practice. Automatic detection of anatomic landmarks from MR images is important for structural and functional analysis of the heart. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, context, around the target. Conventional context is associated with each individual landmark to encode local shape and appearance evidence. We extend this concept to a landmark set , where multiple landmarks have connections at the semantic level, e.g., landmarks belonging to the same anatomy. We propose a joint context approach to construct contextual regions between landmarks. A discriminative model is learned to utilize inter-landmark features for landmark set detection as an entirety. This helps resolve ambiguities of individual landmark detection results. A probabilistic boosting tree is used to learn a discriminative model based on contextual features. We adopt a marginal space learning strategy to efficiently learn and search a high dimensional parameter space. A fully automatic system is developed to detect the set of three landmarks of the left ventricle, the apex and the two basal annulus points, from a single cardiac MR long axis image. We test the proposed approach on a database of 795 long axis images from 116 patients. A 4-fold cross validation results show that about 15% reduction of the errors is obtained by integrating joint context into a conventional landmark detection system.