Automatic identification and extraction of bone contours from x-ray images is the first essential 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 initialization is solved by an Estimation of Bayesian Network Algorithm to fit a multiple component geometrical model to the x-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Our experimental results demonstrate its performance and efficacy even when part of the images are occluded. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Dong, X., & Zheng, G. (2008). Automatic extraction of proximal femur contours from calibrated X-ray images using 3D statistical models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5128 LNCS, pp. 421–429). https://doi.org/10.1007/978-3-540-79982-5_46
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