Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features
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.
KeywordsX-ray segmentation femur landmark detection random forest statistical shape model
Unable to display preview. Download preview PDF.
- 2.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)CrossRefGoogle Scholar
- 7.Gall, J., Lempitsky, V.: Class-specific Hough forests for object detection. In: CVPR 2009, pp. 1022–1029 (2009)Google Scholar
- 8.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, vol. I, pp. 886–893 (2005)Google Scholar