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首页> 外文期刊>Image Processing, IET >Wavelet domain Bayesian processor for speckle removal in medical ultrasound images
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Wavelet domain Bayesian processor for speckle removal in medical ultrasound images

机译：小波域贝叶斯处理器用于医学超声图像去斑

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摘要

Speckle reduction is a prerequisite for many ultrasound image processing tasks. In this study, the authors introduce a novel speckle suppression method for ultrasound images, based on statistical modelling of wavelet coefficients. First, the authors demonstrate that the wavelet coefficients of log-transformed ultrasound images have significantly non-Gaussian statistics and a two-dimensional heteroscedasticity exists in them. Previously, they described the overall multiscale wavelet transform of the logtransformed ultrasound images using two-dimensional generalised autoregressive conditional heteroscedastic (2D GARCH) model. In this study, they introduce a new heteroscedastic model, that is, 2D-GARCH generalised Gaussian (2D-GARCHGG) as an extension of 2D-GARCH model. This new model can capture heavy-tailed marginal distribution and the intrascale dependencies of wavelet coefficients. Also, 2D-GARCH-GG model introduces additional flexibility in the model formulation in comparison with 2D-GARCH model, which results in better characterisation of ultrasound images subbands and improved restoration in noisy environments. In consequence, the authors introduce maximum a-posteriori estimator, based on GARCHGG modelling to estimate the clean wavelet coefficients. In order to evaluate the performance of the proposed method in speckle suppression, they compare it with other denoising methods applied on some artificially speckled and actual ultrasound images and we verify the performance improvement in utilising the new strategies.
机译：减少斑点是许多超声图像处理任务的前提。在这项研究中，作者介绍了一种基于小波系数统计模型的超声图像斑点抑制方法。首先，作者证明对数变换后的超声图像的小波系数具有明显的非高斯统计量，并且其中存在二维异方差。以前，他们使用二维广义自回归条件异方差（2D GARCH）模型描述了对数变换后的超声图像的整体多尺度小波变换。在这项研究中，他们介绍了一种新的异方差模型，即2D-GARCH广义高斯（2D-GARCHGG）作为2D-GARCH模型的扩展。该新模型可以捕获重尾边缘分布和小波系数的尺度内相关性。此外，与2D-GARCH模型相比，2D-GARCH-GG模型在模型制定中引入了更多的灵活性，从而可以更好地表征超声图像子带并改善在嘈杂环境中的恢复。因此，作者基于GARCHGG建模引入了最大的后验估计量，以估计干净的小波系数。为了评估该方法在散斑抑制方面的性能，他们将其与应用于某些人工斑点和实际超声图像的其他降噪方法进行了比较，并验证了在使用新策略时的性能改进。

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