Automatic extraction of proximal femur contours from calibrated X-ray images: a Bayesian inference approach

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 initialisation 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 X-ray images and the statistical model, in which bone contours are extracted by a graphical-model-based Bayesian inference. Experiments on clinical data set verified its robustness against occlusion.