  In this study we investigate the potential of parametric images formed from
ultrasound B-mode scans using the Nakagami distribution for non-invasive
classification of breast lesions. Through a sliding window technique, we
generated seven types of parametric images from each patient scan in our
dataset using basic and as well as derived parameters of the Nakagami
distribution. To determine the most suitable window size for image generation,
we conducted an empirical analysis using three windows, and selected the best
one for our study. From the parametric images formed for each patient, we
extracted a total of 72 features. Feature selection was performed to find the
optimum subset of features for the best classification performance.
Incorporating the selected subset of features with the Support Vector Machine
(SVM) classifier, and by tuning the decision threshold, we obtained a maximum
classification accuracy of 93.08%, an Area under the ROC Curve (AUC) of 0.9712,
a False Negative Rate of 0%, and a very low False Positive Rate of 8.65%. Our
results indicate that the high accuracy of such a procedure may assist in the
diagnostic process associated with detection of breast cancer, as well as help
to reduce false positive diagnosis.
