  Ultrasound imaging plays an important role in breast lesion differentiation.
However, diagnostic accuracy depends on ultrasonographer experience. Various
computer aided diagnosis systems has been developed to improve breast cancer
detection and reduce the number of unnecessary biopsies. In this study, our aim
was to improve breast lesion classification based on the BI-RADS (Breast
Imaging - Reporting and Data System). This was accomplished by combining the
BI-RADS with morphological features which assess lesion boundary. A dataset of
214 lesion images was used for analysis. 30 morphological features were
extracted and feature selection scheme was applied to find features which
improve the BI-RADS classification performance. Additionally, the best
performing morphological feature subset was indicated. We obtained a better
classification by combining the BI-RADS with six morphological features. These
features were the extent, overlap ratio, NRL entropy, circularity,
elliptic-normalized circumference and the normalized residual value. The area
under the receiver operating curve calculated with the use of the combined
classifier was 0.986. The best performing morphological feature subset
contained six features: the DWR, NRL entropy, normalized residual value,
overlap ratio, extent and the morphological closing ratio. For this set, the
area under the curve was 0.901. The combination of the radiologist's experience
related to the BI-RADS and the morphological features leads to a more effective
breast lesion classification.
