Automatic extraction of femur contours from calibrated x-ray images: A Bayesian inference approach

Abstract — Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The automatic initialization to align the 3D model with the x-ray images is solved by an Estimation of Bayesian Network Algorithm to fit a simplified multiple component geometrical model of the proximal femur to the x-ray data. Landmarks can be extracted from the geometrical model for the initialization of the 3D statistical model. The contour extraction is then accomplished by a joint registration and segmentation procedure. We iteratively updates the extracted bone contours and an instanced 3D model to fit the x-ray images. Taking the projected silhouettes of the instanced 3D model on the registered x-ray images as templates, bone contours can be extracted by a graphical model based Bayesian inference. The 3D model can then be updated by a non-rigid 2D/3D registration between the 3D statistical model and the extracted bone contours. Preliminary experiments on clinical data sets verified its validity. Index Terms — contour extraction, statistical model, Bayesian network, 2D/3D registration, segmentation, calibrated x-ray image I