Thakur A, Anand R S
Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India.
J Med Eng Technol. 2007 Jul-Aug;31(4):263-79. doi: 10.1080/03091900600718402.
This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.
本文讨论了一种利用超声医学图像中散斑图案的二阶统计量来减少散斑的自适应滤波技术。已经开发了几种基于区域的自适应滤波技术用于散斑噪声抑制，但在滤波器的后处理中没有选择区域生长大小的具体标准。适合一个局部区域的大小可能不适用于其他区域。选择正确的区域大小涉及在散斑减少和边缘保留之间进行权衡。通常，使用大的区域大小来平滑散斑，而使用小的区域大小来将边缘保留在图像中。在本文中，一种平滑过程结合了用于同质性测试的散斑一阶统计量和用于选择滤波器及期望区域生长的二阶统计量。在区域收缩和区域生长过程中，为每个区域计算灰度共生矩阵（GLCM）以进行二阶统计量计算。此外，这些GLCM特征确定用于区域平滑的合适滤波器。使用边缘保留和散斑减少测试将该方法的性能与激进区域生长滤波器（ARGF）进行比较。处理后的图像结果表明，所提出的方法有效地减少了散斑噪声并保留了边缘细节。
