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Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. This paper proposed a 3D statistical model based framework for the proximal femur bone contour extraction from calibrated x-ray images. The initialization to align the statistical model is solved by a particle filter on a dynamic Bayesian network to fit a multiple component geometrical model to the x-ray images. The contour extraction is accomplished by a non-rigid 2D/3D registration between the 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Experiments on clinical data set verified its robustness against occlusion.