Denoising of low dose CT images using mask non-harmonic analysis with edge-preservation segmentation and whitening filter

Abstract

Computed tomography (CT) imaging acquires patient images using radiation. However, scanning with high doses of radiation can pose a risk to health, because of radiation hazards. Although the risk to the human body during a CT scan can be reduced by reducing the amount of radiation, the quality of the acquired images may deteriorate. Recently, denoising methods using nonlocal means or block matching and 3D filtering were demonstrated to be effective for denoising CT images. These methods performed denoising by adapting to the noise level, according to the position of the image. However, CT images exhibit different magnitudes of noise at different spatial frequencies, as can be observed in their noise power spectrum. Therefore, a method operating in the frequency space, which can accurately model the CT noise and reduce it effectively, is necessary. In this paper, we present a CT denoising method based on edge-preservation segmentation and denoising using mask nonharmonic analysis (mask NHA). Mask NHA can accurately analyze frequencies with high resolutions when applied to the edge preservation area. By using a whitening filter, we provide noise reduction for specific CT noises and improve the image quality of low-dose CT images. A denoising simulation was performed on a standard-dose CT image to which CT noise was added and the performance of the proposed method was compared to that of conventional methods. The proposed method was found to improve the peak signal-to-noise ratio by 3 to 5 dB, compared to the conventional mask NHA.