Manually induced artefacts, like caliper marks and anatomical labels, render an ultrasound (US) image incapable of being subjected to further processes of Computer Aided Diagnosis (CAD). In this paper, we propose a technique to remove these artefacts and restore the image as accurately as possible. The technique finds application as a pre-processing step when developing unsupervised segmentation algorithms for US images that deal with automatic estimation of the number of segments and clustering. The novelty of the algorithm lies in the image processing pipeline chosen to automatically identify the artefacts and is developed based on the histogram properties of the artefacts. The algorithm was able to successfully restore the images to a high quality when it was executed on a dataset of 18 US images of the thyroid gland on which the artefacts were induced manually by a doctor. Further experiments on an additional dataset of 10 unmarked US images of the thyroid gland on which the artefacts were simulated using Matlab showed that the restored images were again of high quality with a PSNR > 38 dB and free of any manually induced artefacts.

中文翻译：

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自动去除甲状腺超声图像中的人工伪像。

诸如卡尺标记和解剖标签之类的人工制品使超声波（US）图像无法进行计算机辅助诊断（CAD）的进一步处理。在本文中，我们提出了一种消除这些伪影并尽可能准确地还原图像的技术。当为美国图像开发无监督的分割算法时，该技术可作为预处理步骤找到应用，该算法可处理自动估计的分割数和聚类。该算法的新颖之处在于选择了可自动识别伪影的图像处理管道，并且基于伪影的直方图属性进行了开发。该算法在由医生人工诱导的人工制品的18个甲状腺甲状腺美国图像的数据集上执行时，能够成功地将图像恢复为高质量。使用Matlab在其中模拟伪影的10个未标记美国甲状腺图像的附加数据集上进行的进一步实验显示，恢复的图像再次具有PSNR> 38 dB的高质量，并且没有任何人工诱发的伪影。