BACKGROUND The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. METHOD The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. RESULTS The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. CONCLUSION Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

中文翻译：

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一种增强的自适应非局部均值算法，用于磁共振脑图像中的Rician降噪。

背景技术在磁共振（MR）成像中形成的里氏噪声大大降低了后续分析的准确性和可靠性，并且大多数现有的降噪方法适合于高斯噪声而不是里氏噪声。为了解决这个问题，我们提出了一种模糊c均值和自适应非局部均值（FANLM）方法，将自适应非局部均值（NLM）与模糊c均值（FCM）相结合，作为一种减少噪声的新方法。研究。方法该算法基于噪声方差自动选择搜索窗口的最佳大小，该噪声方差是由改进的Rician噪声中位数绝对偏差（MAD）估计器估计的。同时解决了传统的NLM算法必须使用固定大小的搜索窗口的问题。考虑到每个像素的分布特性，我们设计了三种类型的搜索窗口大小，分别是大，中和小，而不是使用固定大小。另外，由于改进后的FCM算法划分了图像的隶属度，并引入了形态学重构来保留图像细节，因此与FCM算法的结合有助于达到更好的去噪效果。结果实验结果表明，该算法可以有效地去除噪声。此外，与其他三种方法相比，它具有最高的峰值信噪比（PSNR）和结构相似性（SSIM）：非局部均值（NLM），线性最小均方误差（LMMSE）和未抽取小波变换（UWT） 。使用FANLM方法，可以很好地保留图像细节，而噪点几乎可以消除。结论与传统的去噪方法相比，实验结果表明，该方法有效地抑制了噪声，并很好地保留了边缘细节。但是，FANLM方法在整个实验过程中平均花费13 s，并且其计算成本并不是最短的。解决这些问题可能是我们未来研究的一部分。