Artifact Suppressed Nonlinear Diffusion Filtering for Low-Dose CT Image Processing

Abstract

Computed tomography (CT) images with a low-dose protocol generally have severe mottle noise and streak artifacts. In this paper, we propose a novel diffusion method named ''artifact suppressed nonlinear diffusion filtering (ASNDF),'' to process low-dose CT (LDCT) images. Different from other diffusion filtering methods, the proposed ASNDF not only includes image gradient as the main cue to construct a diffusion coefficient function, but also incorporates the local variances of image to be diffused and residual image between two adjacent diffusions. In detail, the classical PM diffusion is first performed to get the initial residual image, and then from the second iteration, the LDCT image is processed according to the ASNDF processing. Simulated data, clinical data and rat data are conducted to evaluate the proposed method, and the comparison experiments with other competing methods show that the proposed ASNDF method makes an improvement in artifact suppression and structure preservation, and offers a sound alternative to process LDCT images from most current CT systems.

High precision visual localization method of UAV based on feature matching

“…First, it is necessary to carry out nonlinear diffusion filtering on the image. The advantage of a nonlinear diffusion filtering (Li et al, 2016 ; Feng and Chen, 2017 ; Jubairahmed et al, 2019 ; Liu et al, 2019c ) algorithm is that it can filter the image noise while preserving important boundary and other details. The nonlinear diffusion filtering algorithm is mainly a diffusion process in which the gray image changes at different scales are expressed as flow functions.…”