  Intensity variations in image texture can provide powerful quantitative
information about physical properties of biological tissue. However, tissue
patterns can vary according to the utilized imaging system and are
intrinsically correlated to the scale of analysis. In the case of ultrasound,
the Nakagami distribution is a general model of the ultrasonic backscattering
envelope under various scattering conditions and densities where it can be
employed for characterizing image texture, but the subtle intra-heterogeneities
within a given mass are difficult to capture via this model as it works at a
single spatial scale. This paper proposes a locally adaptive 3D
multi-resolution Nakagami-based fractal feature descriptor that extends
Nakagami-based texture analysis to accommodate subtle speckle spatial frequency
tissue intensity variability in volumetric scans. Local textural fractal
descriptors - which are invariant to affine intensity changes - are extracted
from volumetric patches at different spatial resolutions from voxel
lattice-based generated shape and scale Nakagami parameters. Using ultrasound
radio-frequency datasets we found that after applying an adaptive fractal
decomposition label transfer approach on top of the generated Nakagami voxels,
tissue characterization results were superior to the state of art. Experimental
results on real 3D ultrasonic pre-clinical and clinical datasets suggest that
describing tumor intra-heterogeneity via this descriptor may facilitate
improved prediction of therapy response and disease characterization.
