Spatial statistics based feature descriptor for RF ultrasound data

Klein, Tassilo; Hansson, Mattias; Navab, Nassir

doi:10.1109/isbi.2011.5872348

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Necrotic core was not considered in that method. Finally, Bouhlel and Sevestre-Ghalila, 2009 , Klein et al, 2011 describe a Markov random field model combined with Nakagami distribution estimation to differentiate malignant melanoma from normal tissue. However it was found that the estimated scale model parameter was highly sensitive to image quality, and hence subtle variations could go unnoticed.…”

Section: Related Workmentioning

confidence: 99%

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization

Al-Kadi

Chung

Carlisle

et al. 2015

Medical Image Analysis

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Section: Related Workmentioning

confidence: 99%

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization

Al-Kadi

Chung

Carlisle

et al. 2015

Medical Image Analysis

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“…One approach uses the local power spectral density to estimate the integrated backscatter and attenuation coefficient [5,6,7,8], or to measure the mean central frequency and scatterer size [9,10,11,12]. Textural properties of the tissue spatial arrangement can also be estimated from the envelope-detected RF image [13,14]. As the first-order statistical properties of the backscattered RF signal rely on the number density and spatial distribution of scatterers [15,16] which maybe coherent, random or a mixture of both -it would be difficult to account for all scatterer conditions using the former approaches.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors

Al-Kadi

Chung

Coussios

et al. 2016

Ultrasound in Medicine & Biology

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Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results-with nearly similar performance-in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy.

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