Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging : Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings |
Editors | Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan |
Number of pages | 8 |
Publisher | Springer |
Publication date | 2011 |
Pages | 10-17 |
ISBN (Print) | 978-3-642-24318-9 |
ISBN (Electronic) | 978-3-642-24319-6 |
DOIs | |
Publication status | Published - 2011 |
Event | International Workshop on Machine Learning in Medical Imaging - Toronto, Canada Duration: 18 Sep 2011 → 18 Sep 2011 Conference number: 2 |
Conference
Conference | International Workshop on Machine Learning in Medical Imaging |
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Nummer | 2 |
Land | Canada |
By | Toronto |
Periode | 18/09/2011 → 18/09/2011 |
Series | Lecture notes in computer science |
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Volume | 7009 |
ISSN | 0302-9743 |
Bibliographical note
Del af 14th International Conference on Medical
Image Computing and Computer-Assisted Intervention (MICCAI)
ID: 168782252