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Confidence of model based shape reconstruction from sparse data

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks and assign a confidence value to the resulting reconstructed shape. An evaluation study is performed to compare three methods used for sparse SSM fitting w.r.t. specificity, generalization ability, and correctness of estimated confidence limits with an increasing amount of input information. We find that the proposed constrained shape model outperforms the other models, is robust against the selection and amount of sparse information, and indicates the shape confidence well.

OriginalsprogEngelsk
Titel2010 IEEE International Symposium on Biomedical Imaging : from nano to macro
Antal sider4
ForlagIEEE
Publikationsdato2010
Sider1077-1080
ISBN (Trykt)978-1-4244-4125-9
ISBN (Elektronisk)978-1-4244-4126-6
DOI
StatusUdgivet - 2010
Begivenhed7th IEEE International Symposium on Biomedical Imaging: from nano to macro - Congress Center "De Doelen", Rotterdam, Holland
Varighed: 14 apr. 201017 apr. 2010
Konferencens nummer: 7

Konference

Konference7th IEEE International Symposium on Biomedical Imaging
Nummer7
LokationCongress Center "De Doelen"
LandHolland
ByRotterdam
Periode14/04/201017/04/2010

ID: 19119936