Multiscale Nakagami parametric imaging for improved liver tumor localization

TL;DR

This paper introduces a multiscale adaptive Nakagami parametric imaging method that enhances liver tumor localization in ultrasound by improving tissue characterization through stable, scale-sensitive parameter estimation.

Contribution

It proposes a novel multiscale maximum likelihood estimation approach for Nakagami parameters, improving stability and accuracy in tissue characterization over fixed-scale methods.

Findings

Enhanced visualization of tissue changes

Improved tumor localization in ultrasound images

More stable Nakagami parameter estimation

Abstract

Effective ultrasound tissue characterization is usually hindered by complex tissue structures. The interlacing of speckle patterns complicates the correct estimation of backscatter distribution parameters. Nakagami parametric imaging based on localized shape parameter mapping can model different backscattering conditions. However, performance of the constructed Nakagami image depends on the sensitivity of the estimation method to the backscattered statistics and scale of analysis. Using a fixed focal region of interest in estimating the Nakagami parametric image would increase estimation variance. In this work, localized Nakagami parameters are estimated adaptively by means of maximum likelihood estimation on a multiscale basis. The varying size kernel integrates the goodness-of-fit of the backscattering distribution parameters at multiple scales for more stable parameter estimation. Results show improved quantitative visualization of changes in tissue specular reflections, suggesting a potential approach for improving tumor localization in low contrast ultrasound images.