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Despeckling with Structure Preservation in Clinical Ultrasound Images Using Historical Edge Information Weighted Regularizer

机译:使用历史边缘信息加权正则化器对临床超声图像中的结构保留进行去斑

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This article presents a de-speckling technique for clinical ultrasound images with an aim to preserve the fine structural information and region boundaries in images. The algorithm generates restored images by minimizing the variational energy on them. To compute variational energy, a weighted total variation based method is proposed where the weights are determined from both historical (previous/earlier time stamp) as well as instantaneous oriented structural information of images. This helps in defining the anistropy at edges in the image which, in turn, helps in identifying homogenous regions on it. Moreover, the method is able to preserve the vague echo-textural differences which might be of clinical importance but may get destroyed due to smoothing operations. To elicit effectiveness, comparative analysis of the proposed approaches have been done with four state-of-the-art techniques on both in silico and in vivo ultrasound images using four standard measures (two for phantom images and two for clinical ultrasound images). Qualitative and quantitative analysis reveals the promising performance of the proposed technique.
机译:本文提出了一种用于临床超声图像的去斑点技术,旨在保留图像中的精细结构信息和区域边界。该算法通过最小化图像上的变异能量来生成图像。为了计算变分能量,提出了一种基于加权总变分的方法,其中从历史(先前/较早的时间戳)以及图像的瞬时定向结构信息中确定权重。这有助于在图像的边缘处定义人工血管,进而有助于识别其上的同质区域。而且,该方法能够保留模糊的回声-纹理差异,这可能具有临床重要性,但是由于平滑操作而可能被破坏。为了获得效果,已使用四种标准方法(两种用于幻像图像,另外两种用于临床超声图像)对计算机和体内超声图像使用四种最新技术对提出的方法进行了比较分析。定性和定量分析揭示了所提出技术的有希望的性能。

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