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.
Chapter PDF
Similar content being viewed by others
References
Chen, Y., Ee, X., Leow, W.-K., Howe, T.S.: Automatic extraction of femur contours from hip X-ray images. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 200–209. Springer, Heidelberg (2005)
Lindner, C., Thiagarajah, S., Wilkinson, J.M., Wallis, G.A., Cootes, T.F.: Accurate fully automatic femur segmentation in pelvic radiographs using regression voting. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 353–360. Springer, Heidelberg (2012)
Gottschling, H., Roth, M., Schweikard, A., Burgkart, R.: Intraoperative, fluoroscopy-based planning for complex osteotomies of the proximal femur. Int. J. Med. Robot. 1(3), 67–73 (2005)
Baka, N., Kaptein, B.L., Bruijne, M., van Walsum, T., Giphart, J.E., Niessen, W.J., Lelieveldt, B.P.: 2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape model. Med. Image Anal. 15(6), 840–850 (2001)
Dong, X., Zheng, G.: Automatic extraction of proximal femur contours from calibrated X-ray images using 3D statistical models: an in vitro study. Int. J. Med. Robot. 5(2), 213–222 (2009)
Cristinacce, D., Cootes, T.: Automatic feature localization with constrained local models. Pattern Recognition 41(19), 3054–3067 (2008)
Zhou, S.K., Comaniciu, D.: Shape regression machine. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 13–25. Springer, Heidelberg (2007)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation of 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE T. Med. Imaging 27(11), 1668–1681 (2008)
Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Möller, A.M., Nekolla, S., Navab, N.: Fast multiple organ detection and localization in whole-body MR Dixon sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Kokiopoulou, E., Chen, J., Saad, Y.: Trace optimization and eigenproblems in dimension reduction methods. Numerical Linear Algebra with Applications 18(3), 565–602 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Cootes, T.F., Taylor, C.J.: Active shape models-‘smart snakes’. In: BMVC (1992)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Sparse shape composition: a new framework for shape prior modeling. In: CVPR (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, C., Xie, W., Franke, J., Grützner, P.A., Nolte, LP., Zheng, G. (2013). Fully Automatic X-Ray Image Segmentation via Joint Estimation of Image Displacements. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)