Ansatte – Københavns Universitet

Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Peter Mysling, Peter Kersten Petersen, Mads Nielsen, Martin Lillholm

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.
OriginalsprogEngelsk
TitelMachine Learning in Medical Imaging : Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings
RedaktørerKenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan
Antal sider8
ForlagSpringer
Publikationsdato2011
Sider10-17
ISBN (Trykt)978-3-642-24318-9
ISBN (Elektronisk)978-3-642-24319-6
DOI
StatusUdgivet - 2011
BegivenhedInternational Workshop on Machine Learning in Medical Imaging - Toronto, Canada
Varighed: 18 sep. 201118 sep. 2011
Konferencens nummer: 2

Konference

KonferenceInternational Workshop on Machine Learning in Medical Imaging
Nummer2
LandCanada
ByToronto
Periode18/09/201118/09/2011
NavnLecture notes in computer science
Vol/bind7009
ISSN0302-9743

ID: 168782252