An Estimation/Correction Algorithm for Detecting Bone Edges in CT Images


The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, the reliability is used to further correct the normal direction. The optimal scale at each point is obtained while estimating the normal direction; this scale is then used in a simple edge detector. Our experimental results have shown that use of this estimated/corrected normal direction improves the segmentation quality by decreasing the number of unexpected edges and discontinuities (gaps) of real contours. The corrected normal direction could also be used in postprocessing to delete false edges. Our segmentation algorithm is automatic, and its performance is evaluated on CT images of the human pelvis, leg, and wrist.

DOI: 10.1109/TMI.2005.850541

Extracted Key Phrases

19 Figures and Tables


Citations per Year

173 Citations

Semantic Scholar estimates that this publication has 173 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Yao2005AnEA, title={An Estimation/Correction Algorithm for Detecting Bone Edges in CT Images}, author={Weiguang Yao and Purang Abolmaesumi and Michael A. Greenspan and Randy E. Ellis}, journal={IEEE transactions on medical imaging}, year={2005}, volume={24 8}, pages={997-1010} }