Use of non-Rayleigh statistics for the identification of tumors in ultrasonic B-scans of the breast

Shankar, P.M.; Reid, John M.; Ortega, H.V.; Piccoli, Catherine W.; Goldberg, Barry B.

doi:10.1109/42.251119

Cited by 161 publications

(89 citation statements)

References 14 publications

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“…Also, the parameter 1/α should be inversely proportional to the number of randomly located scatterers per resolution cell. In fact, from [33,Eq. (4)], the parameter α is of the form α s N s , where N s is the number of randomly located scatterers per resolution cell and α s > 0 is related to the homogeneity of the scattering cross-sections.…”

Section: 22mentioning

confidence: 99%

“…K-distributions and, more generally, homodyned K-distributions turned out to provide a useful model for the received amplitude in a wide range of non-Gaussian scattering configurations, such as optical propagation through turbulent media [27], microwave sea echo [38], land clutter [26] and medical ultrasonics [33,8,23,10,28,25]. See [6] for a presentation of the homodyned K-distribution and related distributions in the context of ultrasound imaging.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation Method of the Homodyned K-Distribution Based on the Mean Intensity and Two Log-Moments

Destrempes

Porée

Cloutier³

2013

SIAM J. Imaging Sci.

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The homodyned K-distribution appears naturally in the context of random walks and provides a useful model for the distribution of the received intensity in a wide range of non-Gaussian scattering configurations, including medical ultrasonics. An estimation method for the homodyned K-distribution based on the first moment of the intensity and two log-moments (XU method), namely the X and U-statistics previously studied in the special case of the K-distribution, is proposed as an alternative to a method based on the first three moments of the intensity (MI method) or the amplitude (MA method), and a method based on the signal-to-noise ratio (SNR), the skewness and the kurtosis of two fractional orders of the amplitude (labeled RSK method). Properties of the X and U statistics for the homodyned K-distribution are proved, except for one conjecture. Using those properties, an algorithm based on the bisection method for monotonous functions was developed. The algorithm has a geometric rate of convergence. Various tests were performed to study the behavior of the estimators. It was shown with simulated data samples that the estimations of the parameters 1/α and 1/(κ + 1) of the homodyned K-distribution are preferable to the direct estimations of the clustering parameter α and the structure parameter κ .33 (RSK), and the relative RMSEs of 1/(κ + 1) were equal to 0.14 (MI), 0.16 (MA), 0.17 (XU) and 0.20 (RSK). The four methods were also tested on simulated ultrasound images with a variable density of periodic scatterers to test images with a coherent component. The addition of noise on ultrasound images was also studied. Results showed that the XU estimator was overall better than the three other ones. Finally, on the simulated ultrasound images, the average computation times per image were equal to 6.0 ms (MI), 8.0 ms (MA), 6.8 ms (XU) and 500 ms (RSK). Thus, a fast, reliable, and novel algorithm for the estimation of the homodyned Kdistribution was proposed.

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Section: 22mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation Method of the Homodyned K-Distribution Based on the Mean Intensity and Two Log-Moments

Destrempes

Porée

Cloutier³

2013

SIAM J. Imaging Sci.

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“…Deviations from such special scattering conditions are called preRayleigh condition when SNR <1.91, and post-Rayleigh condition when SNR>1.91 (Tuthill et al, 1988). The K-distribution has been proposed to model the pre-Rayleigh condition by accounting for low effective scatter density (Shankar et al, 1993;Shankar, 1995), and Rice distribution has been proposed to model the post-Rayleigh condition by accounting for a coherence component due to the presence of a regular structure of scatters within the tissue (Insana et al, 1986;Tuthill et al, 1988). However, the Rician family fails to model the preRayleigh condition, and K-distribution model does not take into account the post-Rayleigh condition.…”

Section: Grey Level Distributionmentioning

confidence: 99%

Automated Segmentation of Real-Time 3D Echocardiography Using an Incompressibility Constraint

Zhu¹,

Papademetris²,

Sinusas³

et al. 2012

Echocardiography - New Techniques

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“…Early diagnosis requires an accurate and reliable diagnostic procedure that allows physicians to distinguish benign from malignant breast tumors, and finding such a procedure is an important goal. Current procedures for early detection and diagnosis of breast cancer include self-examination, mammography (2)(3)(4), and ultrasonography (US) (5). Previously, the recommended role of US was limited to differentiating between cysts and solid masses, evaluating masses in a dense breast, and guiding interventional procedures.…”

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confidence: 99%

Support Vector Machines for Diagnosis of Breast Tumors on US Images

et al. 2003

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Rationale and Objectives. Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness. Materials and Conclusion.The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.

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Use of non-Rayleigh statistics for the identification of tumors in ultrasonic B-scans of the breast

Cited by 161 publications

References 14 publications

Estimation Method of the Homodyned K-Distribution Based on the Mean Intensity and Two Log-Moments

Estimation Method of the Homodyned K-Distribution Based on the Mean Intensity and Two Log-Moments

Automated Segmentation of Real-Time 3D Echocardiography Using an Incompressibility Constraint

Support Vector Machines for Diagnosis of Breast Tumors on US Images

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