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Automatic Reconstruction of a Patient-Specific Surface Model of a Proximal Femur from Calibrated X-Ray Images Via Bayesian Filters.
- Source :
- Advanced Intelligent Computing Theories & Applications. With Aspects of Theoretical & Methodological Issues; 2007, p1094-1102, 9p
- Publication Year :
- 2007
-
Abstract
- Automatic reconstruction of patient-specific 3D bone model from a limited number of calibrated X-ray images is not a trivial task. Previous published works require either knowledge about anatomical landmarks, which are normally obtained by interactive reconstruction, or a supervised initialization. In this paper, we present an automatic 2D/3D reconstruction scheme and show its applications to reconstruct a surface model of the proximal femur from a limited number of calibrated X-ray images. In our scheme, the geometrical parameters of the proximal femur are obtained by using a Bayesian filter based inference algorithm to fit a parameterized multiple-component geometrical model to the input images. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. Here we report the quantitative and qualitative evaluation results on 10 dry cadaveric bones. Compared to the manual initialization, the automated initialization results in a little bit less accurate reconstruction but has the advantages of elimination of user interactions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540741701
- Database :
- Complementary Index
- Journal :
- Advanced Intelligent Computing Theories & Applications. With Aspects of Theoretical & Methodological Issues
- Publication Type :
- Book
- Accession number :
- 33100793
- Full Text :
- https://doi.org/10.1007/978-3-540-74171-8_111