Active shape models—their training and application
Computer Vision and Image Understanding
The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Active Shape Model-Based Segmentation of Digital X-ray Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Image Segmentation by Shape Particle Filtering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
A static SMC sampler on shapes for the automated segmentation of aortic calcifications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical manner. In a first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a shape model sampling scheme. These samples are used to initialize a second phase of active shape model search, under a nonlinear model of vertebra appearance. The search is constrained by a conditional shape model, based on the variability of the coarse spine location estimates. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation accuracy and failure rate.