Heterogeneous tissue characterization using ultrasound: a comparison of fractal analysis backscatter models on liver tumors

by   Omar S. Al-Kadi, et al.

Assessing tumor tissue heterogeneity via ultrasound has recently been suggested for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting 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, while the Lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound RF images of liver tumors (230 and 378 demonstrating respondent and non-respondent cases, respectively). Crossvalidation via leave-one-tumor-out and with different k-folds methodologies using a Bayesian classifier were 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 - for characterizing liver tumor tissue. Accuracy, sensitivity and specificity for the Nkg/NIG were: 85.6 models, such as the Rician, Rayleigh, and K-distribution were found to not be as effective in characterizing the 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.


page 15

page 19

page 20


Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment

The number density of scatterers in tumor tissue contribute to a heterog...

Multiscale Nakagami parametric imaging for improved liver tumor localization

Effective ultrasound tissue characterization is usually hindered by comp...

Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization

Intensity variations in image texture can provide powerful quantitative ...

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

Intensity variations in image texture can provide powerful quantitative ...

Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications

Biodegradable elastic scaffolds have attracted more and more attention i...

A New Method for the High-Precision Assessment of Tumor Changes in Response to Treatment

Imaging demonstrates that preclinical and human tumors are heterogeneous...

A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours

Tissue texture is known to exhibit a heterogeneous or non-stationary nat...

Please sign up or login with your details

Forgot password? Click here to reset