Jahanian Hesamoddin, Soltanian-Zadeh Hamid, Hossein-Zadeh Gholam-Ali
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, Faculty of Engineering, University of Tehran, Tehran, Iran.
J Magn Reson Imaging. 2005 Sep;22(3):381-9. doi: 10.1002/jmri.20392.
To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents.
Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared.
The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis.
More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.
基于标量小波变换和多小波变换获得的多尺度分解，提出新颖的特征空间，以解决功能磁共振成像（fMRI）时间序列高维问题（当直接用于聚类算法时）以及其低信噪比（SNR）问题，该问题限制了根据fMRI时间序列的激活内容进行准确分类。
所提出的方法通过随机化找到代表fMRI时间序列激活内容的小波/多小波系数，并将它们组合以定义新的特征空间。使用模拟和实验fMRI数据集，将所提出的特征空间与互相关（CC）特征空间进行比较，并评估它们的性能。在这些研究中，使用随机化控制误报检测率。为了比较不同方法，估计并比较了使用模拟数据的接收器操作特性（ROC）曲线的几个点。
所提出的特征抑制了混杂信号的影响并提高了激活检测灵敏度。实验结果表明，与传统的CC分析相比，所提出的方法具有更高的灵敏度和鲁棒性。
与CC特征空间相比，使用所提出的特征空间可以实现更准确、更灵敏的激活检测。与标量小波特征相比，多小波特征显示出更高的检测灵敏度。
