Active shape models—their training and application
Computer Vision and Image Understanding
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Combining Elastic and Statistical Models of Appearance Variation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Motion-compensation of cardiac perfusion MRI using a statistical texture ensemble
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
Left Ventricle Tracking Using Overlap Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Left Ventricle Segmentation via Graph Cut Distribution Matching
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Hierarchical vibrations for part-based recognition of complex objects
Pattern Recognition
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Quality of segmentations obtained by 3D Active Appearance Models (AAMs) crucially depends on underlying training data. MRI heart data, however, often come noisy, incomplete, with respiratory-induced motion, and do not fulfill necessary requirements for building an AAM. Moreover, AAMs are known to fail when attempting to model local variations. Inspired by the recent work on split models [1] we propose an alternative to the methods based on pure 3D AAM segmentation. We interconnect a set of 2D AAMs by a 3D shape model. We show that our approach is able to cope with imperfect data and improves segmentations by 11% on average compared to 3D AAMs.