Abstract
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.
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Zheng, G., Dong, X., Nolte, LP. (2006). Robust and Accurate Reconstruction of Patient-Specific 3D Surface Models from Sparse Point Sets: A Sequential Three-Stage Trimmed Optimization Approach. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_9
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DOI: https://doi.org/10.1007/11812715_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37220-2
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