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International Conference on Information Technologies in Biomedicine

ITIB 2022: Information Technology in Biomedicine pp 246–260Cite as

Semantic Segmentation of Abnormal Lung Areas on Chest X-rays to Detect COVID-19

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1429))

Abstract

The main objective of this study was to effectively segment the lung tissue area in chest radiograms (called a chest X-ray: CXR). The results of conducted analysis were related to the requirements of effective detection and description of COVID-19 (C-19) symptoms to support the diagnosis of this disease. The proposed method uses the concept of representing the chest radiogram using a dictionary of matched lung shape patterns in reference CXRs. The initial lung shape approximation is then corrected by non-rigid registration based on the tissue texture distribution. The optimization criteria used emphasize tissue features that may have diagnostic significance. We refer to this as the semantic, more reliable lung segmentation required by C-19 diagnostic support. The obtained efficiency is comparable to the best ML reference methods and not far from the average efficiency of DL methods. The relatively high values of the minimum fit indices demonstrate the stability and reliability of the segmentation performed.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, 75 Koszykowa st., 00-662, Warsaw, Poland

    Artur Przelaskowski, Magdalena Jasionowska-Skop & Grzegorz Ostrek

Authors
  1. Artur Przelaskowski
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  2. Magdalena Jasionowska-Skop
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  3. Grzegorz Ostrek
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Corresponding author

Correspondence to Artur Przelaskowski .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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Przelaskowski, A., Jasionowska-Skop, M., Ostrek, G. (2022). Semantic Segmentation of Abnormal Lung Areas on Chest X-rays to Detect COVID-19. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_21

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