Efficient and robust nonlocal means denoising of MR data based on salient features matching.
Efficient and robust nonlocal means denoising of MR data based on salient features matching.
复制标题
DOI：
10.1016/j.cmpb.2011.07.014
复制
发表时间：
2012-02
影响因子：
6.1
通讯作者：
Westin CF
中科院分区：
文献类型：
--
作者：
Tristán-Vega A;García-Pérez V;Aja-Fernández S;Westin CF
由于其出色的性能，非本地平均值（NLM）过滤器已成为降级医学图像的流行方法。但是，它的沉重计算负载一直是阻止其使用的重要缺点。 NLM通过平均非局部纤维中的像素来起作用，根据它们与感兴趣的像素的相似性加权它们。根据比较位置的局部贴片中相应像素之间的相应像素之间的平方差来评估这种相似性。我们的建议是通过仅检查与像素相关的显着特征的子集来减少此比较的计算负载，这足以估算原始NLM方法中计算的实际差异。相对于原始实施实现的加速度超过一个数量级，与最近的NLM改进相比，我们的方法的速度几乎是两倍。同时，我们从合成和体内实验的证据表明，适当的显着特征的计算使NLM权重的估计更加强大。因此，我们能够通过最近的最新技术状态来改善各种现实的信号噪声比例（如扩散MRI）的结果。最后，计算出的功能的统计表征可以摆脱参数调整常用的某些启发式方法。
The Nonlocal Means (NLM) filter has become a popular approach for denoising medical images due to its excellent performance. However, its heavy computational load has been an important shortcoming preventing its use. NLM works by averaging pixels in nonlocal vicinities, weighting them depending on their similarity with the pixel of interest. This similarity is assessed based on the squared difference between corresponding pixels inside local patches centered at the locations compared. Our proposal is to reduce the computational load of this comparison by checking only a subset of salient features associated to the pixels, which suffice to estimate the actual difference as computed in the original NLM approach. The speedup achieved with respect to the original implementation is over one order of magnitude, and, when compared to more recent NLM improvements for MRI denoising, our method is nearly twice as fast. At the same time, we evidence from both synthetic and in vivo experiments that computing of appropriate salient features make the estimation of NLM weights more robust to noise. Consequently, we are able to improve the outcomes achieved with recent state of the art techniques for a wide range of realistic Signal-to-Noise Ratio scenarios like diffusion MRI. Finally, the statistical characterization of the features computed allows to get rid of some of the heuristics commonly used for parameter tuning.
登录
查看更多内容
影响因子：
10.6
作者：
Coupe, Pierrick;Hellier, Pierre;Barillot, Christian
通讯作者：
Barillot, Christian
影响因子：
3.9
作者：
Mahmoudi, M;Sapiro, G
通讯作者：
Sapiro, G
影响因子：
2.5
作者：
Aja-Fernandez, Santiago;Tristan-Vega, Antonio;Alberola-Lopez, Carlos
通讯作者：
Alberola-Lopez, Carlos
影响因子：
3.9
作者：
Van De Ville, Dimitri;Kocher, Michel
通讯作者：
Kocher, Michel
影响因子：
10.9
作者：
Manjon, Jose V.;Carbonell-Caballero, Jose;Robles, Montserrat
通讯作者：
Robles, Montserrat