Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
The Trimmed Iterative Closest Point Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
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Constructing an accurate patient-specific three-dimensional (3D) bone model from sparse point sets is a challenging task. A priori information is often required to handle this otherwise ill-posed problem. Previously we have proposed an optimal approach for anatomical shape reconstruction from sparse information [1], which uses a dense surface point distribution model (DS-PDM) as the a priori information and formulates the surface reconstruction problem as a three-stage optimal estimation process including (1) affine registration; (2) statistical extrapolation; and (3) kernel-based deformation. In this paper, we propose an important enhancement that enables to realize stable reconstructions and robustly reject outliers. Handling of outliers is a very crucial requirement especially in the surgical scenario. This is achieved by consistently employing the Least Trimmed Squares (LTS) approach with a roughly estimated outlier rate in all three stages of the reconstruction process. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically determine it. Results of testing the new approach on dry cadaveric femurs with different outlier rates are shown.