Automatic Feature Detection in Lung Ultrasound Images using Wavelet and Radon Transforms

TL;DR

This paper presents an automated method using wavelet and Radon transforms to detect key lung ultrasound features, achieving high accuracy and promising F-scores across multiple datasets.

Contribution

It introduces a novel framework combining wavelet and Radon transforms for automatic lung feature detection in ultrasound images, validated on synthetic and real datasets.

Findings

F2-score of 62% for B-lines

F2-score of 86% for A-lines

F2-score of 100% for Pleural line

Abstract

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.