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. © 2013 Springer-Verlag.
CITATION STYLE
Chen, C., Xie, W., Franke, J., Grützner, P. A., Nolte, L. P., & Zheng, G. (2013). Fully automatic X-ray image segmentation via joint estimation of image displacements. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8151 LNCS, pp. 227–234). https://doi.org/10.1007/978-3-642-40760-4_29
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