Abstract
In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.
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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., 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. IJMRCAS 1(3), 33–38 (2005)
Baka, N., Kaptein, B.L., de Bruijne, M., et al.: 2D-3D shape reconstruction of the distal femur from stereo x-ray imaging using statistical shape model. Med. Image Anal. 15(6), 840–850 (2011)
Dong, X., Zheng, G.: Automatic extraction of proximal femur contours from calibrated x-ray images using 3D statistical models: an in vitro study. IJMRCAS 5(2), 213–222 (2009)
Cristinacce, D., Cootes, T.: Automatic feature localization with constrained local models. Pattern Recognition 41(19), 3054–3067 (2008)
Gall, J., Lempitsky, V.: Class-specific Hough forests for object detection. In: CVPR 2009, pp. 1022–1029 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, vol. I, pp. 886–893 (2005)
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Chen, C., Zheng, G. (2013). Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_50
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DOI: https://doi.org/10.1007/978-3-642-39094-4_50
Publisher Name: Springer, Berlin, Heidelberg
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