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Adaptive speckle reduction of ultrasound images based on maximum likelihood estimation

机译:基于最大似然估计的超声图像自适应散斑减少

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摘要

A method has been developed in this paper to gain effective speckle reduction in medical ultrasound images. To exploit full knowledge of the speckle distribution, here maximum likelihood was used to estimate speckle parameters corresponding to its statistical mode. Then the results were incorporated into the nonlinear anisotropic diffusion to achieve adaptive speckle reduction. Verified with simulated and ultrasound images, we show that this algorithm is capable of enhancing features of clinical interest and reduces speckle noise more efficiently than just applying classical filters. To avoid edge contribution, changes of contrast-to-noise ratio of different regions are also compared to investigate the performance of this approach.
机译:本文开发了一种方法,可以有效减少医学超声图像中的斑点。为了充分利用散斑分布的知识,此处使用最大似然来估计与其统计模式相对应的散斑参数。然后将结果合并到非线性各向异性扩散中,以实现自适应散斑减少。通过模拟和超声图像验证,我们证明了该算法比仅应用经典滤波器能够增强临床关注的特征并更有效地减少斑点噪声。为了避免边缘贡献,还比较了不同区域的对比度-噪声比的变化,以研究该方法的性能。

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