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Data-Adaptive Similarity Measures for B-mode Ultrasound Images Using Robust Noise Models
IEEE Journal of Selected Topics in Signal Processing ( IF 7.5 ) Pub Date : 2020-10-01 , DOI: 10.1109/jstsp.2020.3001829
Nora Ouzir , Esa Ollila , Sergiy A. Vorobyov

Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the $t$-distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures.

中文翻译:

使用鲁棒噪声模型的 B 型超声图像的数据自适应相似性测量

超声成像 (UI) 的特点是存在倍增散斑噪声和各种采集伪影。因此,设计超声 (US) 相似性度量需要特别注意。在运动估计的特定背景下,结合 US 特征不仅有益于传统方法,而且有益于对训练数据质量高度敏感的基于学习的方法。从最大似然 (ML) 角度推导相似性度量使我们能够将这些特殊性考虑在内。与经典的瑞利建模相反,所提出的相似性度量包含更现实的散射条件,例如不同的散斑密度和阴影。具体来说,瑞利统计的偏差使用复杂射频 (RF) 信号的 $t$ 分布和回波幅度的 Nakagami-Gamma (NG) 复合模型进行建模。此外,模型参数是逐块学习的,这导致数据自适应相似性度量。在使用合成、幻像以及 2D 和 3D 体内图像的运动估计的背景下研究了所提出的标准。实验结果表明,与经典的基于瑞利的方法和最先进的相似性度量相比,性能和鲁棒性都有所提高。在使用合成、幻像以及 2D 和 3D 体内图像的运动估计的背景下研究了所提出的标准。实验结果表明,与经典的基于瑞利的方法和最先进的相似性度量相比,性能和鲁棒性都有所提高。在使用合成、幻像以及 2D 和 3D 体内图像的运动估计的背景下研究了所提出的标准。实验结果表明,与经典的基于瑞利的方法和最先进的相似性度量相比,性能和鲁棒性都有所提高。
更新日期:2020-10-01
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