A Novel Total Variation Model for Low-Dose CT Image Denoising

Abstract

Low-dose computed tomography (LDCT) images are polluted by mottle noise and streak artifacts. To improve LDCT images quality, this paper proposes a novel total variation (NTV) model. A weighted coefficient of the regularization term of NTV model is constructed by standard deviation, gray-level probability and gradient magnitude to smooth LDCT images adaptively, since the standard deviation and the gray-level probability of detail region are higher than that of the noisy background, and the gradient magnitude of edges is higher than that of the noisy background. Besides, to preserve details and edges effectively, the fidelity term of the proposed NTV model is constructed by the block-matching 3d filter because it performs well in details and edges preservation. The experiments are performed on the computer simulated phantom and the actual phantom. Compared with several other competitive methods, both subjective visual effect and objective evaluation criteria show that the proposed NTV model can improve LDCT images quality more effectively such as noise and artifacts suppression, details, and edges preservation.