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
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Statistics and Computing
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Conditional point distribution models
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Machine learning in medical imaging
Machine Vision and Applications
| Hi-index | 0.00 |
Segmentation of vertebral contours is an essential task in the design of imaging biomarkers for osteoporosis based on vertebra shape or texture. In this paper, we propose a novel automatic segmentation technique which can optionally be constrained by the user. The proposed technique solves the segmentation problem in a hierarchical manner. In the first phase, a coarse estimate of the overall spine alignment and the vertebra locations is computed using a 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. In supplement, we describe an approach for manual initialization of the segmentation procedure as a simple set of constraints on the fully automatic technique. The technique is evaluated on a data base of 157 manually annotated lumbar radiographs, resulting in a final mean point-to-contour error of $$0.81~\pm ~0.98$$ mm for automatic segmentation. The results outperform the previous work in automatic vertebra segmentation in terms of both segmentation accuracy and failure rate, offering a both automatic and semi-automatic approach in one unifying framework.