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

陈. C. Xing, 宋智洋 Song Zhi-yang, 周明全 Zhou Ming-quan, 武仲科 Wu Zhong-ke, 王醒策 Wang Xing-ce
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引用次数: 2

Abstract

We introduce the classical non-local means filtering algorithm and the improved non-local means filtering algorithm with the weight function modified by Manjon. 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 optimizing 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 Manjon non-mean filtering algorithm. The results show that the improved algorithm from either visual or numerical is superior to Manjon non-mean filtering algorithm.
一种面向脑血管分割的改进非局部均值滤波算法
介绍了经典的非局部均值滤波算法和经Manjon修正的权函数改进的非局部均值滤波算法。在本文中,我们提出了不同的权重函数,并使其在保持优化视觉效果和信噪比的时间复杂度的同时,对局部窗口具有旋转移位不变性。通过加入10 ~ 100范围内的高斯加性噪声标准差,将改进算法与传统滤波算法和Manjon非均值滤波算法进行比较。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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