Multiscale Nakagami parametric imaging for improved liver tumor localization
This work addresses the challenge of accurate liver tumor localization in ultrasound imaging, which is incremental as it builds on existing Nakagami parametric imaging with a multiscale approach.
The paper tackled the problem of unstable parameter estimation in Nakagami parametric imaging for ultrasound tissue characterization by introducing a multiscale adaptive estimation method, resulting in improved quantitative visualization of tissue specular reflections for better tumor localization in low-contrast images.
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.