面向脑血管分割的改进型非局部均值滤波算法研究

An improved non-local mean filter filtering algorithm facing the cerebrovascular segmentation

介绍了经典非局部均值滤波算法与Manjon非局部均值滤波算法，改进了非局部均值滤波方法的相似度权值，使算法在具有旋转平移不变性，保持时间复杂度的同时优化了视觉效果与信噪比。实验通过添加噪声标准差从10～100不等的高斯加性噪声，比较了改进后的算法与传统滤波算法以及Manjon非均值滤波算法，结果表明，改进后的算法无论从视觉上还是数值上都优于Manjon非均值滤波算法。

We introduce the classical non-local means filtering algorithm and the improved non-local means filtering algorithm with the weight function modified by Manj on.In this paper, we propose different weight function , and make it have rotating shift invariance for the local windows while keeping the time complexity of opti -mizing the visual effect and SNR .By adding noise standard deviation from Gaussian additive noise ranging from 10 to 100 , we compare the improved algorithms with traditional filtering algorithms and Manj on non-mean filtering algorithm.The results show that the improved algorithm from either visual or numerical is superior to Manjon non-mean filtering algorithm .