Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization

Zhou, Zhuhuang; Gao, Anna H.; Wu, Weiwei; Tai, Dar‐In; Tseng, Jeng‐Hwei; Wu, Shuicai; Tsui, Po-Hsiang

doi:10.1016/j.ultras.2020.106308

Cited by 27 publications

(25 citation statements)

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“…Recently, a parameter estimator of the HK distribution based on an ANN was proposed, which has a much faster computing speed and a smaller normalized standard deviation for parameters α and k compared with the RSK and XU estimators. 36 The architecture of the ANN is shown in Figure 1, which is a four-layer feed-forward backpropagation (BP) network. It consists of an input layer, two hidden layers, and an output layer.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…This estimator can be easily implemented using the MATLAB platform. More information can be found in Zhou et al 36 in detail.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Moreover, if we restrict the average intensity to 1, we can obtain the following expression by using equations (2), (3), and (11). 36…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…They include (i) the estimator based on the signal-to-noise (SNR) of fractional order moments of the amplitude 33 ; (ii) the level-curve estimator based on the SNR, skewness, and kurtosis of the fractional order moments of the amplitude, namely the RSK estimator 34 ; (iii) the estimator based on the mean intensity and two log-moments, namely the XU estimator 35 ; and (iv) the estimator based on an ANN, namely the ANN estimator. 36…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Zhang

et al. 2022

Ultrason Imaging

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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.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…This estimator can be easily implemented using the MATLAB platform. More information can be found in Zhou et al 36 in detail.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Moreover, if we restrict the average intensity to 1, we can obtain the following expression by using equations (2), (3), and (11). 36…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu

Zhang

et al. 2022

Ultrason Imaging

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“…Because of the noninvasive and excellent computing power of computers, its evaluation results are objective, reliable, accurate, and efficient. e purpose of objective evaluation of the voice includes the following aspects: (1) to increase the interest in singing; (2) to protect the tender voice; (3) to improve artistic accomplishment; (4) to evaluate the scope and extent of the disease; (5) to try to diagnose the disease; (6) after predicting and correcting the vocalization problem [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Voice Recognition and Evaluation of Vocal Music Based on Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Artistic voice is the artistic life of professional voice users. In the process of selecting and cultivating artistic performing talents, the evaluation of voice even occupies a very important position. Therefore, an appropriate evaluation of the artistic voice is crucial. With the development of art education, how to scientifically evaluate artistic voice training methods and fairly select artistic voice talents is an urgent need for objective evaluation of artistic voice. The current evaluation methods for artistic voices are time-consuming, laborious, and highly subjective. In the objective evaluation of artistic voice, the selection of evaluation acoustic parameters is very important. Attempt to extract the average energy, average frequency error, and average range error of singing voice by using speech analysis technology as the objective evaluation acoustic parameters, use neural network method to objectively evaluate the singing quality of artistic voice, and compare with the subjective evaluation of senior professional teachers. In this paper, voice analysis technology is used to extract the first formant, third formant, fundamental frequency, sound range, fundamental frequency perturbation, first formant perturbation, third formant perturbation, and average energy of singing acoustic parameters. By using BP neural network methods, the quality of singing was evaluated objectively and compared with the subjective evaluation of senior vocal professional teachers. The results show that the BP neural network method can accurately and objectively evaluate the quality of singing voice by using the evaluation parameters, which is helpful in scientifically guiding the selection and training of artistic voice talents.

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Ultrasound fuzzy entropy imaging based on time-series signal for tissue characterization

Zhang,

Huang,

et al. 2024

Applied Acoustics

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Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization

Cited by 27 publications

References 50 publications

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

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

Voice Recognition and Evaluation of Vocal Music Based on Neural Network

Ultrasound fuzzy entropy imaging based on time-series signal for tissue characterization

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