几种图像去噪算法的仿真分析及其在fMRI数据处理中的应用

Simulation analysis of several image denoising algorithms and its application in fMRI data processing

目的去除图像噪声是医学图像处理过程中的基本预处理步骤,对图像的后继分析处理的质量有重大影响。本文基于图像去噪和医学图像的诊断准确率息息相关这一现实问题,对几种图像去噪算法进行仿真分析,并实现功能磁共振(functional magnetic resonance imaging,f MRI)数据应用。方法首先阐述了几种常用图像去噪算法的基本原理,其次使用不同算法对加入高斯噪声的Lena图像进行去噪仿真,并对不同结果的峰值信噪比(peak signal-to-noise ratio,PSNR)和均方差(mean square error,MSE)进行比较,最后总结并选择最优降噪算法应用于f MRI数据分析中,以期获得较好的后期处理基础。结果小波分层阈值算法在f MRI处理中的峰值信噪比和均方差更优。结论在f MRI图像去噪过程中,利用小波分层阈值算法更能提高图像的信息利用率,有助于提高医师诊断的准确率。

Objective Image denoising is a basic pretreatment of medical image processing,which has a significant impact on the quality of subsequent analysis and processing of the image. Certain analysis and simulation of the image denoising algorithms are made in this paper. Methods Firstly,we describe the basic principles of several common image denoising algorithms. Secondly,we use different algorithms to do denoising simulation with the Lena image added with Gaussian noise,and then compare the different results of peak signal-to-noise ratio（ PSNR） and mean square error（ MSE）. Finally,we summarize and choose the optimal image denoising algorithm in the application of functional magnetic resonance imaging（ fMRI） data analysis to obtain better post-processing foundation. Results The results of PSNR and MSE by using wavelet hierarchical threshold algorithm in fMRI are better. Conclusions In fMRI denoising, wavelet hierarchical threshold algorithm can improve the utilization of image information and increase the physician’s diagnostic accuracy.