Instituto Politecnico Nacional, Av. Santa Ana 1000, Mexico City 04440, Mexico.
Instituto Mexicano del Petroleo, Mexico City 07730, Mexico.
Sensors (Basel). 2022 Jul 7;22(14):5113. doi: 10.3390/s22145113.
This work proposes a novel scheme for speckle suppression on medical images acquired by ultrasound sensors. The proposed method is based on the block matching procedure by using mutual information as a similarity measure in grouping patches in a clustered area, originating a new despeckling method that integrates the statistical properties of an image and its texture for creating 3D groups in the BM3D scheme. For this purpose, the segmentation of ultrasound images is carried out considering superpixels and a variation of the local binary patterns algorithm to improve the performance of the block matching procedure. The 3D groups are modeled in terms of grouped tensors and despekled with singular value decomposition. Moreover, a variant of the bilateral filter is used as a post-processing step to recover and enhance edges' quality. Experimental results have demonstrated that the designed framework guarantees a good despeckling performance in ultrasound images according to the objective quality criteria commonly used in literature and via visual perception.
本文提出了一种新的用于抑制超声传感器获取的医学图像中散斑的方案。所提出的方法基于块匹配过程，使用互信息作为相似性度量，在聚类区域中将补丁分组，从而产生一种新的去斑方法，该方法将图像及其纹理的统计特性集成到 BM3D 方案中，以创建 3D 组。为此，考虑超像素和局部二值模式算法的变体来执行超声图像的分割，以提高块匹配过程的性能。3D 组是根据分组张量进行建模的，并通过奇异值分解进行去斑处理。此外，双边滤波器的变体被用作后处理步骤，以恢复和增强边缘的质量。实验结果表明，根据文献中常用的客观质量标准和视觉感知，所设计的框架保证了超声图像中良好的去斑性能。
