ISSN 0021-3454 (print version)
ISSN 2500-0381 (online version)

vol 63 / September, 2020

DOI 10.17586/0021-3454-2018-61-10-908-914

UDC 53.047


I. V. Molodkin
Altai State University, Department of General and Experimental Physics; Altai Regional Oncological Dispensary; Medical Physicist;

A. M. Shayduk
Altai State University, Department of General and Experimental Physics; Altai State Medical University, Department of Physics and Informatics; Professor

S. A. Ostanin
Altai State University, Department of Applied Informatics in Economics; LLC "Altai Geophysical Plant"; Chief Engineer

V. K. Konovalov
Altai State Medical University, Department of Radiology; Professor

S. L. Leonov
Polzunov Altai State Technical University, Department of Automated Production Technology; Professor

O. V. Borisenko
Altai State Medical University;

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Abstract. A two-dimensional array of densitometric parameters obtained by processing tomographic medical images of patients with verified diagnoses of lung diseases (cancer and tuberculosis) is analyzed. The possibility of using information about the properties of contours of lung spherical formations is investigated. A technique for constructing contours of such formations based on the analysis of digitized medical images is applied. A mathematical method introduced for describing the contour is based on calculating the conditional center of mass of the image of the pathology and obtaining two one-dimensional signature functions, a module of the radius vector drawn from the center of mass and its polar angle, as functions of the conditional number of the pixel belonging to the contour. Using the spectral analysis of digitized medical images, such characteristics of the contours of the formations under investigation as the spectral entropy of the contour, multiplicity, and the number of local maxima, are calculated. Analysis of statistical difference of these characteristics for different types of diseases (cancer and tuberculosis) was carried out. It is shown that several studied characteristics of the contours are statistically different and therefore enable differential diagnosis of lung disease. 
Keywords: computer tomography, medical images, contour of lung spherical formation, differential diagnostics, spectral analysis, cancer, tuberculosis

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