Статья

Geometric analysis of pathological changes in lungs using CT images for COVID-19 diagnosis

A. Kents, Y. Hamad, K. Simonov, A. Zotin, M. Kurako,
2021

The study is devoted to the analysis of dynamic changes in computer tomography (CT) images of lungs, with the presence of changes associated with COVID-19 in patients with the data confirmed by laboratory diagnostics. The assessment is carried out using the developed computational tools for visualizing pathological changes in lungs. For these purposes it is proposed to use algorithms for noise reduction, contrast enhancement, segmentation and spectral decomposition (shearlet transform). On this computational basis, we propose a methodology for geometric (texture) analysis for highlighting and contrasting local objects of interest, taking into account color coding. The results of the experimental study show that the developed computational technique is an effective tool for visualizing and analyzing the variability of the geometric (texture) features of the studied images, as well as for the dynamic analysis in time and prediction of possible outcomes. Copyright © 2020 for this paper by its authors.

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Версии

  • 1. Version of Record от 2021-08-23

Метаданные

Об авторах
  • A. Kents
    Federal Siberian Scientific and Clinical Center FMBA of Russia, 24 Kolomenskaya st., Krasnoyarsk, 660037, Russian Federation
  • Y. Hamad
    Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation
  • K. Simonov
    Institute of Computational Modelling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
  • A. Zotin
    Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy pr., Krasnoyarsk, 660037, Russian Federation
  • M. Kurako
Предметная рубрика
  • COVID-19
Название журнала
  • CEUR Workshop Proceedings
Том
  • 2727
Страницы
  • 43-50
Ключевые слова
  • Computerized tomography; Data handling; Diagnosis; Geometry; Information analysis; Medical computing; Noise abatement; Textures; Computational technique; Computational tools; Computer tomography images; Contrast Enhancement; Geometric analysis; Pathological changes; Shearlet transforms; Spectral decomposition; Image analysis
Издатель
  • CEUR-WS
Тип документа
  • Conference Paper
Тип лицензии Creative Commons
  • CC-BY
Правовой статус документа
  • Свободная лицензия
Источник
  • scopus