  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.
