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

Author(s)	Soma Chatterjee, Rohit Kamal Chatterjee, R T Goswami
Country	India
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.
Keywords	Ground Glass Opacities (GGO), COVID-19 pneumonia, Chest CT scans, Mathematical morphology, Frangi Multiscale Vesselness Measure
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-05
Cite This	Detection of Ground Glass Opacities in COVID-19 Lung CT Images using Frangi Multiscale Vesselness Measure - Soma Chatterjee, Rohit Kamal Chatterjee, R T Goswami - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13030
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.13030
Short DOI	https://doi.org/gtg6p9

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