Automatic reconstruction of a patient-specific surface model of a proximal femur from calibrated X-ray images via Bayesian filters

Automatic reconstruction of patient-specific 3D bone model from a limited number of calibrated X-ray images is riot 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 their 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.