Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts.

Yanqiu Zeng,Yongshuo Zong,Yiwen Lu,Wenbing Zhao,Wei Zhao,Shixiao Xiao,Baocan Zhang,Yonghong Peng,Yimin Ding,Haiping Ren,Yutian Xiao,Guokai Zhang

doi:10.1155/2020/1405647

Abstract

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

Highlights

Magnetic resonance imaging (MRI) is one of the advanced imageological examination methods for modern medicine
MRI uses powerful magnets and computer-generated radio waves instead of injected contrast agents to create multidimensional images of human organs and tissues. It does not damage the body with ionizing radiation, so it is safer than emission computed tomography (ECT)
Medical images are always polluted by various noises during collection, transmission, and storage. e magnitude of MRI data in the presence of noise generally follows a Rician distribution if acquired with single-coil systems [1]

Summary

Introduction

Magnetic resonance imaging (MRI) is one of the advanced imageological examination methods for modern medicine. MRI uses powerful magnets and computer-generated radio waves instead of injected contrast agents to create multidimensional images of human organs and tissues. It does not damage the body with ionizing radiation, so it is safer than emission computed tomography (ECT). For this reason, MRI is frequently used for imaging tests of the brain and spinal cord. Compared with computed tomography (CT), MR image has a lower spatial resolution, longer scan time, and more artifacts. E magnitude of MRI data in the presence of noise generally follows a Rician distribution if acquired with single-coil systems [1]. MR image denoising, as an essential preprocessing step for MRI data processing, has been a hot topic in the related area

Methods

Results

Conclusion