Abstract
In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in order to project the CT data into the sinogram domain. The sinogram of data can be decomposed into its illumination and reflectance components by using the homomorphic wavelet filtering. The investigation of the CT images shows that the degradations caused by metallic artifacts appear mainly in the illumination component. Therefore, in our approach the corrupted illumination component is restored by using the apriori information driven from the previous artifact-free sections. The results show that the metal artifacts are considerably reduced without eliminating the important details of the CT images. The proposed method is also compared with other existing methods on a set of dental CT images. Comparisons show the superiority of the proposed method over other existing methods.
Highlights
Computed Tomography (CT) is one of the most useful imaging procedures in medical imaging [1,2, 3]
They have been applied to several data taken from a dataset containing a series of 930 real dental CT images among which 341 images are affected by the metal implants
We follow an application-based quantitative evaluation in order to provide a comparison between the proposed method and three other artifact-reduction approaches [5], [19] [20]] by using the corrupted and artifact-reduced clinical data as well as their corresponding reference ground truth maps defined on uncorrected images
Summary
The performance evaluation of the proposed approach as well as some of the competing artifact reduction methods are presented . We follow an application-based quantitative evaluation in order to provide a comparison between the proposed method and three other artifact-reduction approaches [5], [19] [20]] by using the corrupted and artifact-reduced clinical data as well as their corresponding reference ground truth maps defined on uncorrected images. To this end, 20 dental CT images which have been corrupted by metal artifacts are randomly chosen from the database of the Face and Jaw Radiography Center. Average classification accuracies and reliabilities (in percent) as well as the standard deviations (S.D) of the gained results are reported for 20 corrupted dental CT slices before and after applying the artifact reduction methods
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