An Improved Parameter Estimator of the Homodyned K Distribution Based on the Maximum Likelihood Method for Ultrasound Tissue Characterization

IF 2.5 4区 医学 Q1 ACOUSTICS
Yang Liu, Yufeng Zhang, Bingbing He, Zhiyao Li, Xun Lang, Hong Liang, Jianhua Chen
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引用次数: 1

Abstract

The homodyned K distribution (HK) can generally describe the ultrasound backscatter envelope statistics distribution with parameters that have specific physical meaning. However, creating robust and reliable HK parameter estimates remains a crucial concern. The maximum likelihood estimator (MLE) usually yields a small variance and bias in parameter estimation. Thus, two recent studies have attempted to use MLE for parameter estimation of HK distribution. However, some of the statements in these studies are not fully justified and they may hinder the application of parameter estimation of HK distribution based on MLE. In this study, we propose a new parameter estimator for the HK distribution based on the MLE (i.e., MLE1), which overcomes the disadvantages of conventional MLE of HK distribution. The MLE1 was compared with other estimators, such as XU estimator (an estimation method based on the first moment of the intensity and tow log-moments) and ANN estimator (an estimation method based on artificial neural networks). We showed that the estimations of parameters α and k are the best overall (in terms of the relative bias, normalized standard deviation, and relative root mean squared errors) using the proposed MLE1 compared with the others based on the simulated data when the sample size was N = 1000. Moreover, we assessed the usefulness of the proposed MLE1 when the number of scatterers per resolution cell was high (i.e., α up to 80) and when the sample size was small (i.e., N = 100), and we found a satisfactory result. Tests on simulated ultrasound images based on Field II were performed and the results confirmed that the proposed MLE1 is feasible and reliable for the parameter estimation from the ultrasonic envelope signal. Therefore, the proposed MLE1 can accurately estimate the HK parameters with lower uncertainty, which presents a potential practical value for further ultrasonic applications.
基于极大似然法的改进的同差K分布参数估计方法用于超声组织表征
同动K分布(HK)一般可以描述具有特定物理意义参数的超声后向散射包络统计分布。然而,创建稳健可靠的HK参数估计仍然是一个关键问题。极大似然估计器(MLE)在参数估计中通常产生较小的方差和偏差。因此,最近有两项研究尝试使用MLE对HK分布进行参数估计。然而,这些研究中的一些说法并不完全合理,可能会阻碍基于MLE的HK分布参数估计的应用。本文提出了一种基于最大似然估计(MLE1)的HK分布参数估计方法,克服了传统HK分布最大似然估计的不足。将MLE1与XU估计(一种基于强度一阶矩和两个对数矩的估计方法)和ANN估计(一种基于人工神经网络的估计方法)等其他估计方法进行了比较。我们发现,当样本量为N = 1000时,与基于模拟数据的其他方法相比,使用所提出的MLE1对参数α和k的总体估计(在相对偏差、标准化标准差和相对均方根误差方面)是最好的。此外,我们评估了所提出的MLE1在每个分辨率单元的散射体数量高(即α高达80)和样本量小(即N = 100)时的有效性,我们发现了令人满意的结果。对基于Field II的模拟超声图像进行了测试,结果证实了所提出的MLE1对超声包络信号参数估计的可行性和可靠性。因此,所提出的MLE1可以准确估计HK参数,不确定度较低,对进一步的超声应用具有潜在的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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