  Objective: Lung ultrasonography is a significant advance toward a harmless
lung imagery system. This work has investigated the automatic localization of
diagnostically significant features in lung ultrasound pictures which are
Pleural line, A-lines, and B-lines. Study Design: Wavelet and Radon transforms
have been utilized in order to denoise and highlight the presence of clinically
significant patterns. The proposed framework is developed and validated using
three different lung ultrasound image datasets. Two of them contain synthetic
data and the other one is taken from the publicly available POCUS dataset. The
efficiency of the proposed method is evaluated using 200 real images. Results:
The obtained results prove that the comparison between localized patterns and
the baselines yields a promising F2-score of 62%, 86%, and 100% for B-lines,
A-lines, and Pleural line, respectively. Conclusion: Finally, the high F-scores
attained show that the developed technique is an effective way to automatically
extract lung patterns from ultrasound images.
