Detection of Ground Glass Opacities in COVID-19 Lung CT Images using Frangi Multiscale Vesselness Measure

Abstract

In this work, an automated technique for the quick and accurate detection of Ground Glass Opacities (GGO) in chest CT images of COVID-19 pneumonia patients is presented. The method uses mathematical morphology-based methods and Otsu's thresholding during the segmentation of GGO regions in order to extract the lung fields. The program uses the Frangi Multiscale Vesselness Measure to identify and remove these structures based on the anatomical features of bronchioles. Using a dataset of 155 lung CT images, the study outperformed previous algorithms in terms of sensitivity, specificity, and accuracy, all exceeding 97.22%. Radiologists can be helped by this automated screening method to quickly find GGOs in lung CT scans.