Burr, Lomax, Pareto, and Logistic Distributions from Ultrasound Speckle

Parker, Kevin J.; Poul, Sedigheh S.

doi:10.1177/0161734620930621

Cited by 17 publications

(14 citation statements)

References 56 publications

(98 reference statements)

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“…Parker et al 25 derived the first order speckle statistics of biological tissue in ultrasound imaging under the assumptions of weak scattering (using the Born approximation) originating from fractal branching of vasculature represented by cylinders. This derivation leads to power-law functions that dictate the PDFs for the echo amplitude and intensity histograms.…”

Section: Modeling Of Biological Tissuementioning

confidence: 99%

“…A power law distribution and spatial correlation function are also consistent with generalized fractal models. 26 Thus, in scanning a volume, the probability of encountering a scatterer of characteristic dimension 𝑎 is given as 25,27 𝑝(𝑎) = 𝑏 − 1 𝑎 min ( 𝑎 𝑎 min )…”

Section: Modeling Of Biological Tissuementioning

confidence: 99%

“…Recently, a new model hypothesized that in normal soft tissue, the dominant scattering elements are cylinders from fractal branching vasculature. [22][23][24][25] As a result of this model containing governing power-law relationships, three new and distinct probability distributions were used to characterize ultrasound speckle in biological tissue. These were the Burr type XII distribution, the Lomax distribution, and the generalized logistic distribution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Speckle statistics of biological tissues in optical coherence tomography

Rolland

Parker

2021

Biomed. Opt. Express

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The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. A recent hypothesis implies that underlying power-law distributions in the medium structure, such as the fractal branching vasculature, will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. In this study, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various biological tissues and samples. The distributions are also compared with conventional distributions such as the Rayleigh, K, and gamma distributions. The results indicate that these newer distributions based on power laws are, in general, more appropriate models and support the plausibility of their use for characterizing biological tissue. Potentially, the governing power-law parameter of these distributions could be used as a biomarker for tissue disease or pathology.

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Section: Modeling Of Biological Tissuementioning

confidence: 99%

Section: Modeling Of Biological Tissuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speckle statistics of biological tissues in optical coherence tomography

Rolland

Parker

2021

Biomed. Opt. Express

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“…A form of the Burr type XII distribution was recently used to model OCT speckle data. 29 , 35 It corresponds to a two-parameter distribution for non-negative random variables, where one of its shape parameters (

) is set to 2 and can be described as

where

is the power law parameter and

is the scale parameter. This distribution was used by Ge et al 29 to model speckle OCT data in a gelatin phantom with milk, and a set of ex vivo tissue: mouse brain, mouse liver, pig cornea, and chicken muscle.…”

Section: Resultsmentioning

confidence: 99%

Signal-carrying speckle in optical coherence tomography: a methodological review on biomedical applications

et al. 2022

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. Significance: Speckle has historically been considered a source of noise in coherent light imaging. However, a number of works in optical coherence tomography (OCT) imaging have shown that speckle patterns may contain relevant information regarding subresolution and structural properties of the tissues from which it is originated. Aim: The objective of this work is to provide a comprehensive overview of the methods developed for retrieving speckle information in biomedical OCT applications. Approach: PubMed and Scopus databases were used to perform a systematic review on studies published until December 9, 2021. From 146 screened studies, 40 were eligible for this review. Results: The studies were clustered according to the nature of their analysis, namely static or dynamic, and all features were described and analyzed. The results show that features retrieved from speckle can be used successfully in different applications, such as classification and segmentation. However, the results also show that speckle analysis is highly application-dependant, and the best approach varies between applications. Conclusions: Several of the reviewed analyses were only performed in a theoretical context or using phantoms, showing that signal-carrying speckle analysis in OCT imaging is still in its early stage, and further work is needed to validate its applicability and reproducibility in a clinical context.

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“…Our goal is to improve the diagnosis of breast lesions imaged using conventional ultrasound scanners. To do this, we combine a number of recently developed biophysics-based analyses of ultrasound echoes, specifically the H-scan analysis and the Burr analysis of speckle (Parker and Baek 2020 , Parker and Poul 2020 ), along with additional morphological measures of the lesion. These are combined mathematically using principal component analysis (PCA) and are separated in a multiparametric space by the SVM.…”

Section: Introductionmentioning

confidence: 99%

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

Baek

O’Connell

Parker

2022

Mach. Learn.: Sci. Technol.

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The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to further-more provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were seg-mented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature’s data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic (ROC) curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the prob-ability and Breast Imaging Reporting and Data System (BI-RADS) enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

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Burr, Lomax, Pareto, and Logistic Distributions from Ultrasound Speckle

Cited by 17 publications

References 56 publications

Speckle statistics of biological tissues in optical coherence tomography

Speckle statistics of biological tissues in optical coherence tomography

Signal-carrying speckle in optical coherence tomography: a methodological review on biomedical applications

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

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