Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International KES Conference on Intelligent Decision Technologies, KES-IDT 2021

Год издания: 2021

Идентификатор DOI: 10.1007/978-981-16-2765-1_14

Ключевые слова: color-coding, covid-19, ct image, ct image features, fibrosis, image analysis, lung pathologies

Аннотация: 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.

Журнал: Smart Innovation, Systems and Technologies

Выпуск журнала: Vol. 238

Номера страниц: 173-183

ISSN журнала: 21903018

Издатель: Springer Science and Business Media Deutschland GmbH

• Zotin A. (Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Pr, Krasnoyarsk, 660037, Russian Federation)
• Kents A. (Federal Siberian Research Clinical Centre, FMBA of Russia, 26 Kolomenskaya st, Krasnoyarsk, 660037, Russian Federation)
• Simonov K. (Institute of Computational Modeling of the SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation)
• Hamad Y. (Siberian Federal University, 79 Svobodny Pr, Krasnoyarsk, 660041, Russian Federation)

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