PropertyValue
?:abstract
  • 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
is ?:annotates of
?:creator
?:journal
  • 1st_Siberian_Scientific_Workshop_on_Data_Analysis_Technologies_with_Applications,_SibDATA_2020
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • Geometric analysis of pathological changes in lungs using CT images for COVID-19 diagnosis
?:type
?:who_covidence_id
  • #937992
?:year
  • 2020

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