IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Shape Particle Filtering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
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
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
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
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
Heterogeneous computing for vertebra detection and segmentation in x-ray images
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
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The aim of the work is to provide a fully automatic method of segmenting vertebrae in spinal radiographs. This is of clinical relevance to the diagnosis of osteoporosis by vertebral fracture assessment, and to grading incident fractures in clinical trials. We use a parts based model of small vertebral patches (e.g. corners). Many potential candidates are found in a global search using multi-resolution normalised correlation. The ambiguity in the possible solution is resolved by applying a graphical model of the connections between parts, and applying geometric constraints. The resulting graph optimisation problem is solved using loopy belief propagation. The minimum cost solution is used to initialise a second phase of active appearance model search. The method is applied to a clinical data set of computed radiography images of lumbar spines. The accuracy of this fully automatic method is assessed by comparing the results to a gold standard of manual annotation by expert radiologists.