Abstract
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.
Originalsprog | Engelsk |
---|---|
Titel | Machine Learning in Medical Imaging : Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings |
Redaktører | Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan |
Antal sider | 8 |
Forlag | Springer |
Publikationsdato | 2011 |
Sider | 10-17 |
ISBN (Trykt) | 978-3-642-24318-9 |
ISBN (Elektronisk) | 978-3-642-24319-6 |
DOI | |
Status | Udgivet - 2011 |
Begivenhed | International Workshop on Machine Learning in Medical Imaging - Toronto, Canada Varighed: 18 sep. 2011 → 18 sep. 2011 Konferencens nummer: 2 |
Konference
Konference | International Workshop on Machine Learning in Medical Imaging |
---|---|
Nummer | 2 |
Land/Område | Canada |
By | Toronto |
Periode | 18/09/2011 → 18/09/2011 |
Navn | Lecture notes in computer science |
---|---|
Vol/bind | 7009 |
ISSN | 0302-9743 |
Bibliografisk note
Del af 14th International Conference on MedicalImage Computing and Computer-Assisted Intervention (MICCAI)