ABSTRACT
The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.
ABSTRACT
The paper is devoted to the methods of interpretation and analysis of dynamic changes associated with COVID-19 in CT images of the lungs. An attempt was made to identify possible regularities of the CT pattern and diagnose the possible development of fibrosis in the early stages. To improve the accuracy of diagnosis and prognosis of the formation and development of fibrosis, we propose the creation of the Feature Atlas of CT images with a specific X-ray state. An experimental study within the framework of the formed dataset, divided into 4 groups according to the severity of changes, was carried out. The preliminary results of processing and texture (geometric) analysis of CT images were obtained. The analysis of a series of CT images includes key steps such as preprocessing, segmentation of lung regions and color coding, as well as calculation cumulative assessment of features to highlight areas with probable pathology, combined assessment of features and the formation of the Feature Atlas. We generated the preliminary Feature Atlas for automation and more accurate analysis of the CT images set. As part of the study on the selected groups of patients, the areas with the probabilities of pathologies associated with COVID-19 development were identified. The study shows the dynamics of residual reticular changes. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In recent years computed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks such as highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the images. © Authors 2021. CC BY 4.0 License.
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.