Differentiation of COVID-19 Conditions using Mediastinum Shape in Chest X-ray Images

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 Xray 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Differentiation of COVID-19 Conditions using Mediastinum Shape in Chest X-ray Images ; volume:8 ; number:2 ; year:2022 ; pages:325-328 ; extent:4 ; Current directions in biomedical engineering ; 8, Heft 2 (2022), 325-328 (gesamt 4)

Contributors, Places and Time

• Creator: Kumar Tulo, Sukanta ; Govindarajan, Satyavratan ; Ramu, Palaniappan ; Swaminathan, Ramakrishnan

Further information

• DOI: 10.1515/cdbme-2022-1083
• URN: urn:nbn:de:101:1-2022090315252886225662
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:20 AM CEST