fMRI time series stepwise denoising based on wavelet transform

doi:10.11772/j.issn.1001-9081.2016.09.2601

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2601-2604.DOI: 10.11772/j.issn.1001-9081.2016.09.2601

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fMRI time series stepwise denoising based on wavelet transform

LI Weiwei, MEI Xue, ZHOU Yu

College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing Jiangsu 211816, China

Received:2016-02-17 Revised:2016-04-11 Online:2016-09-10 Published:2016-09-08
Supported by:
This work is partially supported by the Six Talent Peaks Project in Jiangsu Province 2015 Foundation (XXRJ-012).

基于小波变换的功能磁共振图像时间序列分步去噪

李微微, 梅雪, 周宇

南京工业大学电气工程与控制科学学院, 南京 211816

通讯作者: 梅雪
作者简介:李微微(1992-),女,江苏南通人,硕士研究生,主要研究方向:模式识别、图像处理;梅雪(1975-),女,山东青岛人,副教授,博士研究生,主要研究方向:模式识别、图像处理、数据挖掘;周宇(1992-),男,河南南阳人,硕士研究生,主要研究方向:模式识别、图像处理。
基金资助:
2015年江苏省六大人才高峰项目（XXRJ-012）。

Abstract

Abstract: The neural activity signal of interest is often influenced by structural noise and random noise in functional Magnetic Resonance Imaging (fMRI) data. In order to eliminate noise effects in the analysis of activate voxels, the time series of voxels preprocessed by Statistical Parametric Mapping (SPM) were transformed by Activelets wavelet. After getting scale coefficient and detail coefficient, the two kinds of noise denoised were eliminated separately according to their corresponding characteristics. Firstly, the Independent Component Analysis (ICA) was used to identify and eliminate the structural noise sources. Secondly, an improved algorithm for spatial correlation was presented on the detail coefficient. In particular, in the improved algorithm, the voxel similarity in the neighborhood was used to determine whether the detail coefficient reflected the noise or the neural activity. Experimental results show that the processing of data effectively eliminate the effect of noise; specifically, the frame displacement decreased by 1.5mm and the percentage of spikes decreased by 2%; in addition, the false activation regions are obviously restrained in the spatial map got by denoised signals.

Key words: functional Magnetic Resonance Imaging (fMRI), denoising, structural noise, random noise, wavelet transform

摘要： 功能磁共振图像（fMRI）数据中反映大脑神经活动的感兴趣信号常受到结构噪声和随机噪声的影响。为消除上述噪声对分析激活体素的影响，对经过SPM标准预处理的体素时间序列进行Activelets小波变换，并在得到尺度系数及细节系数后，针对两类噪声的不同特点进行分步去噪。第一步，在受结构噪声影响的尺度系数上，选用独立成分分（ICA）析去识别并消除结构噪声源；第二步，提出一种改进的空域相关去噪算法在细节系数上对信号进行处理。值得注意的是，该算法利用邻域体素之间的相似性，判定所处位置的细节系数反映噪声还是神经活动。实验结果表明，经过这两步处理的数据可有效消除噪声的影响，其中框架位移减少了1.5mm，尖峰百分比减少了2%，此外由去噪后的信号获得的脑激活图中一些明显的伪激活区得到抑制。

关键词: 功能磁共振图像, 去噪, 结构噪声, 随机噪声, 小波变换

CLC Number:

TP391.4

LI Weiwei, MEI Xue, ZHOU Yu. fMRI time series stepwise denoising based on wavelet transform[J]. Journal of Computer Applications, 2016, 36(9): 2601-2604.

李微微, 梅雪, 周宇. 基于小波变换的功能磁共振图像时间序列分步去噪[J]. 计算机应用, 2016, 36(9): 2601-2604.

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fMRI time series stepwise denoising based on wavelet transform

基于小波变换的功能磁共振图像时间序列分步去噪

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