  Quantitative ultrasound (QUS) based parameters like the effective scatterer
diameter (ESD) and mean scatterer spacing (MSS) are gaining attention recently
as non-invasive biomarkers for soft tissue characterization. In this work, we
propose a multiple QUS parameter based technique that employs ESD and MSS, for
binary classification of breast lesions. In order to produce improved ESD
estimates, we propose a modified frequency domain technique for ESD estimation
of breast tissues from the diffuse component of backscattered radio-frequency
(RF) data. Ensemble empirical mode decomposition (EEMD) is performed to
separate the diffuse component from the coherent component by decomposing the
RF data into their intrinsic mode functions (IMFs). A non-parametric
Kolmogorov-Smirnov (K-S) test is employed for automatic IMF selection along
with a multi-step system effect minimization process. The ESD is estimated
using a nearest neighborhood average regression line fitting algorithm.
Furthermore, we use an ameliorated EEMD domain autoregressive (AR) spectral
estimation technique for MSS estimation. On using the ESD for binary
classification of 159 lesions, we obtain high sensitivity, specificity,
accuracy values of 91.07%, 96.12%, and 94.34%, respectively, with an area under
the receiver operating characteristics (ROC) curve of 0.94. On combining ESD
with MSS we obtain even more improved sensitivity, specificity, and accuracy
values of 96.43%, 95.15%, and 95.60%, respectively, with an area under the ROC
of 0.96. Such a high classification performance highlights the potential of
these QUS parameters to be used as non-invasive biomarkers for breast cancer
detection.
