MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
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
Using the Particle Filter Approach to Building Partial Correspondences Between Shapes
International Journal of Computer Vision
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
Vertebral shape: automatic measurement with dynamically sequenced active appearance models
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A framework of vertebra segmentation using the active shape model-based approach
Journal of Biomedical Imaging
Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Quantitative vertebral morphometry using neighbor-conditional shape models
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but can not cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.