Tissue characterization based on scatterer number density estimation

Sleefe, Gerard E.; Lele, P. P.

doi:10.1109/58.9332

Cited by 62 publications

(17 citation statements)

References 5 publications

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“…The pdf is given by [20]

p_{A} (A) = \frac{2 b}{Γ (μ)} {(\frac{bA}{2})}^{μ} K_{μ - 1} (bA)

where Γ(·) is the Gamma function, K n (·) is the modified Bessel function of the second kind, n -th order, and μ is a measure of the effective number of scatterers per resolution cell. In ultrasound, the resolution cell volume can be defined as the volume of the point spread function of the imaging system [22], i.e., the volume of the insonifed medium that contributes to any given point in the echo signal. In (2), the b parameter can be expressed as

b = 2 \sqrt{\frac{μ}{E [A^{2}]}}

where E [·] is the expectation operator.…”

Section: Envelope Statistics Modelsmentioning

confidence: 99%

Improved parameter estimates based on the homodyned K distribution

David

Oelze

2009

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Quantitative techniques based on ultrasound backscatter are promising tools for ultrasonic tissue characterization. There is a need for fast and accurate processing strategies to obtain consistent estimates. An improved parameter estimation algorithm for the homodyned K distribution was developed based on SNR, skewness, and kurtosis of fractional-order moments. From the homodyned K distribution, estimates of the number of scatterers per resolution cell (μ parameter) and estimates of the ratio of coherent to incoherent backscatter signal energy (k parameter) were obtained. Furthermore, angular compounding was used to reduce estimate variance while maintaining spatial resolution of subsequent parameter images. Estimate bias and variance from Monte Carlo simulations were used to quantify the improvement using the new estimation algorithm compared to existing techniques. Improvements due to angular compounding were quantified by the decrease in estimate variance in both simulations and measurements from tissue-mimicking phantoms and by the increase in target contrast. Finally, the new algorithm was used to derive estimates from two kinds of mouse mammary tumors for tissue characterization. The new estimation algorithm yielded estimates with lower bias and variance than existing techniques. For a typical pair of parameters (μ=5 and k=1), the bias and variance were reduced 67% and 16%, respectively, for the μ parameter estimates and 79% and 37%, respectively, for the k parameter estimates. The use of angular compounding further reduced the estimate variance, e.g., the variance of estimates for the μ parameter from measurements was reduced by a factor of approximately 90 when using 120 angles of view. Finally, statistically significant differences were observed in parameter estimates from two kinds of mouse mammary tumors using the new algorithm. These improvements suggest estimating parameters from the backscattered envelope can enhance the diagnostic capabilities of ultrasonic imaging.

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“…The pdf is given by [20]

p_{A} (A) = \frac{2 b}{Γ (μ)} {(\frac{bA}{2})}^{μ} K_{μ - 1} (bA)

b = 2 \sqrt{\frac{μ}{E [A^{2}]}}

where E [·] is the expectation operator.…”

Section: Envelope Statistics Modelsmentioning

confidence: 99%

Improved parameter estimates based on the homodyned K distribution

David

Oelze

2009

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…In an extreme case, the memory mechanism is switched off in regions with no relevant structures for clinical purposes. The memory mechanism is also based on a tensor operator St.u which permits to reach stationary states in tissues where the speckle is intended to be preserved for further analysis or processing [16], [2], [4], [5]. Reaching stationary states provides a robustness that prevents from over-filtering and avoids the necessity of defining a stopping criterion.…”

Section: Discussionmentioning

confidence: 99%

Anisotropic Diffusion Filter With Memory Based on Speckle Statistics for Ultrasound Images

Ramos‐Llordén

Vegas-Sánchez-Ferrero

Martı́n-Fernández

et al. 2015

IEEE Trans. on Image Process.

117

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Ultrasound imaging exhibits considerable difficulties for medical visual inspection and for the development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this work, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. Specifically, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters.

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“…Some methods propose to derive acoustical parameters from the envelope image. They are based on the numerous studies which try to link the particularities of a tissue with a specific intensity distribution on the envelope image [17].…”

Section: Acoustical Parametersmentioning

confidence: 99%