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Fully Automatic X-Ray Image Segmentation via Joint Estimation of Image Displacements
Cheng Chen1, Weiguo Xie1, Jochen Franke2, Paul A. Grützner2, Lutz-P. Nolte1, and Guoyan Zheng1
1Institute for Surgical Technologies and Biomechanics, Universität Bern, Switzerland
2BG Clinic Ludwigshafen, Ludwigshafen, Germany
Abstract. We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.
LNCS 8151, p. 227 ff.
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© Springer-Verlag Berlin Heidelberg 2013