A Novel Denoising Method for Medical CT Images Based on Moving Decomposition Framework

In the past decades, CT images have been widely used and played a critical role in medical diagnosis. However, low-dose CT images are often contaminated by noise, this being the most important factor affecting the quality of a CT image. This paper proposes a novel integrated framework and a denoising method for low-dose medical CT images to obtain a better denoising effect whilst at the same time preserving an image’s local structure information. First, an image moving decomposition is employed to decompose the CT image. The original CT noisy image is decomposed, and the components will be processed separately, so that the details and edges of the CT image can be better preserved. Next, the Shearlet Transformation-based denoising method is applied to the component which contains edges and detailed information of the CT image. The multi-directionality and multi-scale property of the Shearlet make it possible to obtain better effect in denoising the detail parts. BM3D filtering is used to remove noise in the component similar to the origin image, and obtain ideal denoising results in denoising the approximate components (mainly low frequency part) of the CT image. With the two processed components and the inverse decomposition, the denoised image is obtained. Finally, simulations and clinical experiments are conducted and comparisons made. The experimental results show the proposed denoising method can obtain better performances in terms of PSNR value, SSIM and FoM and thus have very competitive results compared with other existing CT denoising methods.