METHODS AND MODELS FOR TEXTURE ANALYSIS OF LUNG PATHOLOGICAL CHANGES BASED ON COMPUTED TOMOGRAPHY FOR COVID-19 DIAGNOSIS
- 1Federal State-Financed Institution Federal Siberian Research Clinical Centre, Federal Medical Biological Agency, 26 Kolomenskaya st., 660037, Krasnoyarsk, Russian Federation
- 2Institute of Space and Information Technologies, Siberian Federal University, 79 Svobodny st., Krasnoyarsk 660041, Russian Federation
- 3Institute of Computational Modelling of the SB RAS, 50/44 Akademgorodok, Krasnoyarsk 660036, Russian Federation
- 4Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037, Russian Federation
Keywords: CT image, Lung Pathology from COVID-19, Pulmonary Fibrosis, Prognosis of outcomes, Texture Image Analysis, Color-coded Contrasting
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.