对数压缩超声图像去斑点的自适应数据拟合模型

IF 1.2 Q2 MATHEMATICS, APPLIED
Yiming Gao
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引用次数: 0

摘要

散斑形成的良好统计模型对于设计用于临床超声图像的良好散斑减少模型是有用的。根据临床超声成像的对数压缩算法,我们提出了一种新的通用分布来描述临床超声图像中散斑的分布。设计了一种新的变分模型来去除具有所提出的一般分布的散斑噪声。通过对合成图像和真实超声图像的实验证实了所提出模型的有效性。与以前作为指定分布符号的变分方法相比,该方法通过估计参数来确定合适的分布,从而自适应地去除不同类型的散斑噪声。实验表明,该方法可以自适应地去除不同类型的散斑噪声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images
. A good statistical model of speckle formation is useful to design a good speckle reduction model for clinical ultrasound images. We propose a new general distribution to describe the distribution of speckle in clinical ultrasound images accord-ing to a log-compression algorithm of clinical ultrasound imaging. A new variational model is designed to remove the speckle noise with the proposed general distribution. The efficiency of the proposed model is confirmed by experiments on synthetic images and real ultrasound images. Compared with previous variational methods which as-sign a designated distribution, the proposed method is adaptive to remove different kinds of speckle noise by estimating parameters to find suitable distribution. The experiments show that the proposed method can adaptively remove different types of speckle noise.
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CiteScore
2.70
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0.00%
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