CT Metal Artifact Reduction Method Based on Improved Image Segmentation and Sinogram In-Painting

Abstract

The streak artifacts caused by metal implants degrade the image quality and limit the applications of CT imaging. The standard method used to reduce these metallic artifacts often consists of interpolating the missing projection data but the result is often a loss of image quality with additional artifacts in the whole image. This paper proposes a new strategy based on a three-stage process: (1) the application of a large-scale non local means filter (LS-NLM) to suppress the noise and enhance the original CT image, (2) the segmentation of metal artifacts and metallic objects using a mutual information maximized segmentation algorithm (MIMS), (3) a modified exemplar-based in-painting technique to restore the corrupted projection data in sinogram. The final corrected image is then obtained by merging the segmented metallic object image with the filtered back-projection (FBP) reconstructed image from the in-painted sinogram. Quantitative and qualitative experiments have been conducted on both a simulated phantom and clinical CT images and a comparative study has been led with Bal's algorithm that proposed a similar segmentation-based method.