Background and objective

Ultrasound is the non-radioactive imaging modality used in the diagnosis of various diseases related to the internal organs of the body. The presence of speckle noise in ultrasound image (UI) is inevitable and may affect resolution and contrast of the image. Existence of the speckle noise degrades the visual evaluation of the image. The despeckling of UI is a desirable pre-processing step in computer-aided UI based diagnosis systems.

Methods

This paper proposes a novel method for despeckling UIs using pre-trained residual learning network (RLN). Initially, RLN is trained with pristine and its corresponding noisy images in order to achieve a better performance. The developed method chooses a pre-trained RLN for despeckling UIs with less computational resources. But the training procedure of RLN from scratch is computationally demanding. The pre-trained RLN is a blind despeckling approach and does not require any fine tuning and noise level estimation. The presented approach shows superiority in the removal of speckle noise as compared to the existing state-of-art methods.

Results

To highlight the effectiveness of the proposed method the pristine images from the Waterloo dataset has been considered. The proposed pre-trained RLN based UI despeckling method resulted in a better peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) at different speckle noise levels. The no-reference image quality approach is adopted to ensure robustness of the established method for real time UI. From results it is obvious that, the performance of the proposed method is superior than the existing methods in terms of naturalness image quality evaluator (NIQE).

Conclusions

From the experimental results, it is clear that the proposed method outperforms the existing despeckling methods in terms of both artificially added and naturally occurring speckle images.

背景和目标

超声是用于诊断与人体内部器官相关的各种疾病的非放射性成像方式。超声图像（UI）中斑点噪声的出现是不可避免的，并且可能会影响图像的分辨率和对比度。斑点噪声的存在降低了图像的视觉评价。UI的去斑点化是基于计算机的基于UI的诊断系统中理想的预处理步骤。

方法

本文提出了一种使用预训练的残差学习网络（RLN）来去除UI斑点的新方法。最初，RLN会接受原始图像及其相应的噪点图像训练，以获得更好的性能。所开发的方法选择预训练的RLN来用较少的计算资源来去除UI的斑点。但是从零开始的RLN训练过程在计算上要求很高。预训练的RLN是一种盲去斑点方法，不需要任何微调和噪声水平估计。与现有的现有技术方法相比，所提出的方法在去除斑点噪声方面显示出优越性。

结果

为了突出所提出方法的有效性，已经考虑了来自滑铁卢数据集的原始图像。所提出的基于RLN的预训练UI去斑点方法在不同斑点噪声水平下产生了更好的峰值信噪比（PSNR）和结构相似性指标测量（SSIM）。采用无参考图像质量方法来确保所建立的实时UI方法的鲁棒性。从结果显而易见，在自然图像质量评估器（NIQE）方面，该方法的性能优于现有方法。

结论

从实验结果来看，很明显，在人工添加和自然出现的斑点图像方面，所提出的方法优于现有的去斑点方法。