A Two-Step Denoising Method for Low Dose Computed Tomography Image via Morphological Component Analysis and Non-Local Means

Low dose computed tomography (LDCT) can reduce the radiation hazard to patients effectively. However, mottle noise and streak artifacts often lower and degenerate the quality of the LDCT image in the process of reconstruction. This article presents a two-step denoising method, which exploits the morphological component analysis (MCA) and non-local means (NLM), for removing the noise and artifacts in LDCT image. In the first step, the MCA-based image separation is performed with the proposed dictionary. The dictionary is firstly established from the learning procedure from the preprocessed images, and then modified by using gradient activity measure. Consequently, the streak artifacts are removed from LDCT image. In the second step, the NLM method is adopted to further remove the mottle noise in the residual image. Experimental results from both simulated phantom and real clinical data demonstrate that compared with several related methods, the proposed method shows superior performance in both noise/artifacts removal and structure preservation.