Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images
Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images
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DOI：
10.1002/jmri.21487
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发表时间：
2008-09-01
影响因子：
4.4
通讯作者：
Ayaz, Muhammad
中科院分区：
文献类型：
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作者：
Pandian, Daniel S. J.;Ciulla, Carlo;Ayaz, Muhammad
目的：创建一种利用复数数据去除噪声像素的稳健方法。材料和方法：使用接收器工作特征(ROC)曲线来确定合适的幅值和相位阈值以及连接值，以确定图像中哪些像素代表噪声。为了对结果进行微调，应用了尖峰去除和空洞替换算子来减少I类错误和去除小的噪声孤岛。结果：利用相位信息通过进一步识别只包含噪声的像素来改进仅基于幅度的阈值方法。利用幅度和相位数据的局部连通性增强了该方法的性能。仿真数据的ROC分析表明，在没有连通性的情况下，第一类误差小于10(-4)，第二类误差小于10(-3)，在信噪比为3：1或更高的情况下，连通性分别为0和10-3。结论：联合使用幅值图像和相位图像有助于提高磁共振图像中噪声点的去除。这在自动可视化相位图像方面可以被证明是有用的，而不会在噪声区域中产生高度分散注意力的相位噪声。此外，当获得可变大小区域的最小强度投影时，它在磁化率加权成像中也很有用。
Purpose: To create a robust means to remove noise pixels using complex data.Materials and Methods: A receiver operating characteristic (ROC) curve was used to determine the appropriate choice of magnitude and phase thresholds as well as connectivity values to determine what pixels represent noise in the image. To fine-tune the results, a spike removal and hole replacement operator is applied to reduce Type I error and remove small islands of noise.Results: The use of phase information improves the magnitude-only thresholding approach by further recognizing pixels that contain only noise. The performance of the method is enhanced using local connectivity of magnitude and phase data. An ROC analysis on simulated data shows that the Type I and Type II errors are less than 10(-4) and 10(-3), respectively, without connectivity and 0 and 10-3, respectively, with connectivity for a signal-to-noise ratio (SNR) of 3:1 or higher.Conclusion: The joint use of both magnitude and phase images helps to improve the removal of noise points in magnetic resonance images. This can prove useful in automating the visualization of phase images without the highly distractive phase noise in noise regions. Also, it is useful in susceptibility weighted imaging when taking the minimum intensity projections of variably sized regions.