摘要
超声波因其所具有的良好指向性及对人体的无伤害性,在检测及医疗领域得到了广泛应用.然而,超声波图像所特有的相干涉斑点噪声成为影响超声波图像质量的主要因素.针对这一问题,提出了将张量投票法应用于超声波图像去噪的方法.利用张量投票法能够提取图像中点、线、面特征的特点,消除超声波图像中孤立点显著性,突出所提取的线、面特征,重新构建图像,从而达到去除斑点噪声的目的.试验结果表明,与其他空间域滤波方法相比,张量投票法在去除斑点噪声的同时,能够更有效保留边缘特征,提高超声波图像的质量.
The inherent speckle noise in ultrasound images severely affects the quality of images.In this paper,an algorithm based on tensor voting to reduce the speckle noise is proposed.Tensor voting presents a unified computation framework that can implement the smoothness constraint to generate structured information,such as isolated points,curves,and surface from a 2-D or 3-D image.Using this feature,the isolated points are exacted by tensor voting and the curves and surface feature are emphasized on to restrain the speckle noise.The experimental result shows that compared with other spatial filter,the algorithm based on tensor voting can more effectively remove the speckle noise and improve the quality of ultrasound images.
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
2014年第1期75-78,共4页
Journal of Henan Polytechnic University（Natural Science）
基金
江苏省自然科学基金资助项目(BK20130235)
东北电力大学博士基金资助项目(BSJXM-201202)
作者简介
郭树强（1976-）,男,吉林省吉林市人,博士,讲师,主要从事图像处理、信号处理研究与教学工作.E-mail：guoshuqiang@mail.nedu.edu.cn
