Confidence of model based shape reconstruction from sparse data
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Confidence of model based shape reconstruction from sparse data. / Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.; Niessen, Wiro; Lelieveldt, B. P. F.
2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE, 2010. p. 1077-1080.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Confidence of model based shape reconstruction from sparse data
AU - Baka, N.
AU - de Bruijne, Marleen
AU - Reiber, J. H. C.
AU - Niessen, Wiro
AU - Lelieveldt, B. P. F.
N1 - Conference code: 7
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
U2 - 10.1109/ISBI.2010.5490179
DO - 10.1109/ISBI.2010.5490179
M3 - Article in proceedings
SN - 978-1-4244-4125-9
SP - 1077
EP - 1080
BT - 2010 IEEE International Symposium on Biomedical Imaging
PB - IEEE
T2 - 7th IEEE International Symposium on Biomedical Imaging
Y2 - 14 April 2010 through 17 April 2010
ER -
ID: 19119936