改进权值非局部均值超声图像去噪  

Improved weighted non-local means ultrasonic image denoising algorithm

作　　者：郑渊悦 徐铭恩[1] 王玲[1] 

机构地区：[1]杭州电子科技大学生命信息与仪器工程学院,杭州310018

出　　处：《中国图象图形学报》2017年第6期778-786,共9页Journal of Image and Graphics

基　　金：国家高技术研究发展计划(863计划)(2015AA020303);国家自然科学基金项目(KYZ193713071;KYZ193716041)~~

摘　　要：目的超声图像斑点噪声会影响诊断的准确性和可靠性。通过分析超声图像斑点噪声统计模型,结合非局部均值滤波算法,提出一种基于超声斑点噪声模型的改进权值非局部均值(NLM)滤波算法。方法算法针对超声图像灰度信息对图像进行预处理,利用超声图像斑点噪声模型改进传统NLM算法的权值计算函数,基于图像特征确定最优采样间隔进行下采样,利用改进后的权值计算函数对图像进行NLM去噪处理。结果分别采用人工合成与真实超声图像对本文算法性能进行测试,并与传统非局部均值滤波算法、非局部总变分(NLTV)等算法进行去噪效果比较,同时采用均方误差、峰值信噪比和平均结构相似性作为滤波算法性能的客观评价指标。本文算法能快速完成超声图像的去噪处理,峰值信噪比较其他算法可以提高0.2 d B以上,可以降低均方误差,提高平均结构相似性,缩短处理时间,并得到较好的图像质量和视觉效果。结论根据超声图像斑点噪声模型对NLM算法的权值计算函数进行优化,使得NLM图像滤波算法能更好地适用于超声图像的去噪,基于超声斑点噪声模型的改进权值NLM算法相较于其他算法,滤波效果更佳,适合超声图像去噪。Objective Medical ultrasound imaging, CT, MR, and X-ray imaging are four modern medical imaging tech- niques. Medical ultrasound imaging techniques are ultrasonic-based diagnostic imaging approaches used to visualize subcu- taneous body structures, such as muscles, vessels, tendons, joints, and internal organs. Compared with other imaging techniques, medical ultrasound imaging is widely used in clinical diagnosis, especially in pregnant women and fetuses, because it is non-invasive, inexpensive, convenient, can be applied in real time, and so on. However, due to the influence of the ultrasonic imaging principle, the ultrasonic image is inevitably disturbed by speckle noise during the generation process, which not only reduces the quality of the ultrasonic image but also makes the identification and analysis of the im- age detail highly difficult. In this study, an improved non-local means （NLM） image denoising algorithm based on the noise model of the ultrasonic image is proposed. Method A statistical model of speckle noise is obtained based on the probability distribution of the ultrasonic image. Then, the Bayesian formula and speckle noise model are utilized to improve the weight function of the NLM filter algorithm. The weight function of the traditional NLM algorithm is based on Gaussian dis- tribution, so it can suppress Gaussian noise well. However, it is unsuitable for speckle noise. In this study, the weight function is improved based on the speckle noise model to make the algorithm applicable to an ultrasonic image. The algo- rithm preprocesses the image according to the characteristics of the proposed weight function by using a pre-defined thresh- old. If the average gray value of the image is greater than 155, then the image is processed directly. If the average gray val- ue of the image is less than 100, then the anti-colored image is used for denoising. If the image has an average gray value of 100 to 155, both the original and anti-colored images are processed, and the average of the results is

关 键 词：图像去噪 非局部均值 斑点噪声 超声图像 下采样 权重函数 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]