基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法

doi:10.11772/j.issn.1001-9081.2017071780

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 848-853.DOI: 10.11772/j.issn.1001-9081.2017071780

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法

方宏道^1,2, 周颖玥^1,2, 林茂松^1,2

1. 西南科技大学信息工程学院, 四川绵阳 621010;
2. 特殊环境机器人技术四川省重点实验室(西南科技大学), 四川绵阳 621010

收稿日期:2017-07-20 修回日期:2017-09-20 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 周颖玥
作者简介:方宏道(1991-),男,安徽池州人,硕士研究生,主要研究方向:数字图像处理;周颖玥(1983-),女,四川马尔康人,副研究员,博士,主要研究方向:图像处理与分析;林茂松(1964-),男,安徽滁州人,教授,博士,CCF会员,主要研究方向:计算机图形图像处理、科学计算可视化。
基金资助:
国家自然科学基金资助项目（61401379）；四川省教育厅项目（14ZB0107）。

Speckle suppression algorithm for ultrasound image based on Bayesian nonlocal means filtering

FANG Hongdao^1,2, ZHOU Yingyue^1,2, LIN Maosong^1,2

1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. Sichuan Provincial Key Laboratory of Robot Technology Used for Special Environment(Southwest University of Science and Technology), Mianyang Sichuan 621010, China

Received:2017-07-20 Revised:2017-09-20 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61401379), the General Project of Educational Commission of Sichuan Province (14ZB0107).

摘要/Abstract

摘要： 超声成像是现代医学影像学最重要的诊断技术之一。然而，由于乘性斑点噪声的存在，使得超声成像的发展受到了一定的限制。针对这种问题，提出了一种贝叶斯非局部平均（NLM）滤波算法的改进策略。首先，运用贝叶斯公式推导出适应于超声图像斑点噪声模型的非局部平均滤波器，由此引出了两种图像块之间距离计算的方式——Pearson距离和根距离；其次，为了减轻计算负担，在非局部区域中选取相似图像块时采用图像块预选择的方式来加速算法；另外，根据多次实验，总结出了一种滤波参数和噪声方差的关系，实现了参数的自适应；最后，利用Visual Studio和OpenCV实现了算法，使得程序的运行时间大幅缩短。为了评估所提算法的去噪性能，在幻影图像和真实超声图像上进行了实验，结果表明：与现有的一些经典算法相比，该算法在去除斑点噪声的表现上有很大提升，并且在保留图像边缘和结构细节方面取得了令人满意的结果。

关键词: 乘性斑点噪声, Pearson距离, 根距离, 块预选择, 参数自适应

Abstract: Ultrasound imaging is one of the most important diagnostic techniques of modern medical imaging. However, due to the presence of multiplicative speckle noise, the development of ultrasound imaging has been limited. For this problem, an improved strategy for Bayesian Non-Local Means (NLM) filtering algorithm was proposed. Firstly,a Bayesian formulation was applied to derive an NLM filter adapted to a relevant ultrasound noise model, which leads to two methods of calculating distance between the image blocks, the Pearson distance and the root distance. Secondly, to lighten the computational burden, a image block pre-selection process was used to accelerate the algorithm when a similar image block was selected in the non-local area. In addition, the relationship between parameter and noise variance was determined experimentally, which made the parameter being adaptive to the noise.Finally, the VS (Visual Studio) and OpenCV (Open source Computer Visual library) were used to realize the algorithm, making the program running time greatly reduced. In order to evaluate the denoising performance of the proposed algorithm, experiments were conducted on both phantom images and real ultrasound images. The experimental results show that the algorithm has a great improvement in the performance of removing speckle noise and achieves satisfactory results in terms of preserving the edges and image details, compared with some existing classical algorithms.

Key words: multiplicative speckle noise, Pearson distance, root distance, block pre-selection, parameter adaptation

中图分类号:

TP391.41

方宏道, 周颖玥, 林茂松. 基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法[J]. 计算机应用, 2018, 38(3): 848-853.

FANG Hongdao, ZHOU Yingyue, LIN Maosong. Speckle suppression algorithm for ultrasound image based on Bayesian nonlocal means filtering[J]. Journal of Computer Applications, 2018, 38(3): 848-853.

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基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法

Speckle suppression algorithm for ultrasound image based on Bayesian nonlocal means filtering

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