Сибирский федеральный университет

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

Конференция: KES International Conference on Intelligent Decision Technologies, KES-IDT 2020

Год издания: 2020

Идентификатор DOI: 10.1007/978-981-15-5925-9_21

Ключевые слова: 3d filters, color coding, ct images, geometric analysis, image enhancement, medical imaging, segmentation, shearlet transform

Аннотация: In recent years, medical technologies aimed at extracting quantitative features from medical images have been greatly developed. One of them is radiomics, which allows extracting a large number of quantitative indicators, based on various features. The extracted data are preliminarily evaluated and visualized to improve decision support by a medical specialist. Paper describes a combination of methods for assessing CT images. Processing is aimed at solving a number of diagnostic tasks, such as highlighting, contrasting objects of interest taking into account color coding, and their further evaluation by corresponding criteria aimed to clarify the nature of the changes and increase both the detectability of pathological changes and the accuracy of the diagnostic conclusion. For these purposes, it is proposed to use pre-processing algorithms that take into account a series of images. Segmentation of lungs and possible pathology area are conducted using wavelet transform and Otsu thresholding. As the means of visualization and feature extraction, it was decided to use delta maps and maps obtained by shearlet transform with color coding. The experimental and clinical material shows the effectiveness of the proposed combination for analyzing the variability of the internal geometric features of the object of interest in the studied images. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2020.

Журнал: Smart Innovation, Systems and Technologies

Выпуск журнала: Vol. 193

Номера страниц: 243-252

ISSN журнала: 21903018

• Zotin A. (Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Pr, Krasnoyarsk, 660037, Russian Federation)
• Hamad Y. (Siberian Federal University, 79 Svobodny St, Krasnoyarsk, 660041, Russian Federation)
• Simonov K. (Institute of Computational Modelling of the SB RAS, 50/44, Akademgorodok, Krasnoyarsk, 660036, Russian Federation)
• Kurako M. (Siberian Federal University, 79 Svobodny St, Krasnoyarsk, 660041, Russian Federation)
• Kents A. (Federal Siberian Scientific and Clinical Center, FMBA of Russia, 24 Kolomenskaya St, Krasnoyarsk, 660037, Russian Federation)

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