An estimation/correction algorithm for detecting bone edges in CT images

TitleAn estimation/correction algorithm for detecting bone edges in CT images
Publication TypeJournal Article
Year of Publication2005
AuthorsYao, W., P. Abolmaesumi, M. Greenspan, and R. E. Ellis
JournalMedical Imaging, IEEE Transactions on
Volume24
Pagination997 -1010
Date Publishedaug.
ISSN0278-0062
KeywordsAlgorithms, anatomy, Artificial Intelligence, Automated, bone, Bone and Bones, bone contour, bone edge detection, computed tomography images, Computer-Assisted, computerised tomography, edge detection, estimated/corrected normal direction, estimation theory, estimation/correction algorithm, human pelvis, Humans, image segmentation, Imaging, Information Storage and Retrieval, leg, medical image processing, Pattern Recognition, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Three-Dimensional, tomography, wrist, X-Ray Computed
Abstract

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.

URLhttp://dx.doi.org/10.1109/TMI.2005.850541
DOI10.1109/TMI.2005.850541

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