波谱学杂志 ›› 2008, Vol. 25 ›› Issue (2): 234-242.

• 研究论文 • 上一篇    下一篇

基于神经网络和小波变换的MRI图像去噪方法

刘小征; 傅彩霞; 杨光   

  1. 华东师范大学 物理系,上海市功能磁共振成像重点实验室,上海 200062
  • 收稿日期:2007-06-04 修回日期:2007-06-19 出版日期:2008-06-05 发布日期:2009-12-05
  • 通讯作者: 杨光

An MRI Image Denoising Algorithm Using Neural Network Analysis and Wavelet Transformation

LIU Xiao-zheng;FU Cai-xia;YANG Guang   

  1. Shanghai Key Laboratory of Functional Magnetic Resonance Imaging, Department of Physics,East China Normal University, Shanghai 200062
  • Received:2007-06-04 Revised:2007-06-19 Online:2008-06-05 Published:2009-12-05
  • Contact: Yang Guang

摘要: 过去10年中,小波变换在图像去噪中取得了很大的成功.人们提出了多种适用于小波去噪的阈值方法,而这些方法就是希望能够正确地反映有噪声小波系数与无噪声小波系数之间的映射关系.基于这种想法,我们提出一种在小波域中利用神经网络寻找这种映射关系的图像去噪新方法.我们把该方法应用于不同噪声分布的磁共振图像的去噪,取得了良好的效果.

关键词: 磁共振成像, 神经网络, 小波变换

Abstract: Wavelet transformation has been widely used for image denoising. Different schemes of noise thresholding have been suggested. A good wavelet thresholding scheme should reflect the correct mapping of the wavelet coefficients of the noised images to that of clean images. In this study, we proposed a new method of image denoising, which utilizes neural networks to search the mapping relations in the wavelet domain. The method was applied to denoise MRI images and proven to be satisfactory in performance and insensitive to noisedistribution.

Key words: magnetic resonance imaging, neural network, wavelet transform