Differentiation of COVID-19 Conditions using Mediastinum Shape in Chest X-ray Images
Current Directions in Biomedical Engineering
; 8(2):325-328, 2022.
Article
in English
| Scopus | ID: covidwho-2054433
ABSTRACT
In this work, an attempt has been made to analyze the shape variations in mediastinum for differentiation of Coronavirus Disease-2019 (COVID-19) and normal conditions in chest X-ray images. For this, the images are obtained from a publicly available dataset. Segmentation of mediastinum from the raw images is performed using Reaction Diffusion Level Set (RDLS) method. Shape-based features are extracted from the delineated mediastinum masks and are statistically analyzed. Further, the features are fed to two classifiers, namely, multi-layer perceptron and support vector machine for differentiation of normal and COVID-19 images. From the results, it is observed that the employed RDLS method is able to delineate mediastinum from the raw chest X-ray images. Eight shape features are observed to be statistically significant. The mean values of these features are found to be distinctly higher for COVID-19 images as compared to normal images. Area under the curve of greater than 76.9% is achieved for both the classifiers. It appears that mediastinum could be used as a region of interest for computerized detection and mass screening of the disease. © 2022 The Author(s), published by De Gruyter.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Current Directions in Biomedical Engineering
Year:
2022
Document Type:
Article
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