基于自适应高斯滤波的超声斑点降噪

医学超声图像存在的斑点噪声降低了图像的质量，给临床诊断和图像的后续处理带来了困难。为了有效地去除噪声，本文提出了一种自适应高斯滤波的超声斑点降噪算法。该算法利用局部特征匹配计算出图像的处理窗口区域与参考区域的相似度，再根据相似度将整幅图像区分为斑点噪声区域和组织区域。同时利用相似度调整高斯滤波器的宽度值，使高斯滤波器对图像的不同区域进行不同程度的过滤。物理体模实验和人体超声肝脏实验结果表明,该算法可以有效地去除超声图像中的斑点噪声并保留组织结构,并且可使迭代次数大大减少，是一种有效的医学超声图像降噪方法。

Speckle Reduction Based on Adaptive Gauss Filtering

Speckle noises in medical ultrasound image would decrease the quality of image and bring difficulties to the analysis and diagnosis of the subsequent image. To reduce speckle, here we propose a speckle reduction algorithm based on the adaptive Gauss filter. The algorithm distinguished the image with speckle regions and characteristic regions by a similarity deprived from local characteristic matching between the processing window and a reference speckle area. According to the similarity, this algorithm adjustes adaptively the width of the Gauss filter. Ultrasound phantom testing and in vivo imaging show that the proposed method is effective. It can reduce the numbers of iteration significantly, as well as the speckle and preserve edge.