Metal Artifact Reduction in Dental Computed Tomography Images Based on Sinogram Segmentation Using Curvelet Transform Followed by Hough Transform

Mehran Yazdi, Maryam Mohammadi


In X-ray computed tomography (CT), the presence of metal objects in a patient’s body producesstreak artifacts in the reconstructed images. During the past decades, many different methods wereproposed for the reduction or elimination of the streaking artifacts. When scanning a patient, theprojectiondata affected by metal objects (missing projections) appear as regions with high intensities in thesinogram. In spiral fan beam CT, these regions are sinusoid-like curves on sinogram. During the firsttime, if the metal curves are detected carefully, then, they can be replaced by correspondingunaffected projections using other slices or opposite views; therefore, the CT slices regenerated bythe modified sonogram will be imaged with high quality. In this paper, a new method of thesegmentation of metal traces in spiral fan–beam CT sinogram is proposed. This method is based on asinogram curve detection using a curvelet transform followed by 2D Hough transform. The initialenhancement of the sinogram using modified curvelet transform coefficients is performed bysuppressing all the coefficients of one band and applying 2D Hough transform to detect moreprecisely metal curves. To evaluate the performance of the proposed method for the detection ofmetal curves in a sinogram, precision and recall metrics are calculated. Compared with othermethods, the results show that the proposed method is capable of detecting metal curves, with betterprecision and good recovery.

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