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

References:

1. Konovalov V.K., Lobanov M.N., Leonov S.L., Shayduk A.M., Kolmogorov V.G., Dombrovskiy A.A. Vestnik altaiskoy nauki, 2013, no. 2-1, pp. 149–153 (in Russ.)
2. Dobrescu R., Dobrescu M., Mocanu M., Popescu D. WSEAS Transactions on Biology and Biomedicine, 2010, no. 3(7), pp. 223–232.
3. Kalender W.A. Computer Tomography: Fundamentals, System Technology, Image Quality, Applications, 2005.
4. Shayduk A.M., Molodkin I.V. Meditsinskaya Fizika (Medical Physics), 2015, no. 2(66), pp. 66–73 (in Russ.)
5. Vlasov P.V. Luchevaya diagnostika zabolevaniy organov grudnoy kletki (Radiodiagnosis of Diseases of Bodies of a Thorax), Moscow, 2008. (in Russ.)
6. Hofer M. CT Teaching Manual, Georg Thieme Verlag, 2000, 176 p.
7. Al-Kadi O.S., Watson D. IEEE Transactions on Biomedical Engineering, 2008, no. 7(55), pp. 1822– 1830.
8. Kirillova O.A., Koshechkina N.A., Volkova L.D., Zakharova E.V. Materialy Vserossiyskogo kongressa luchevykh diagnostov (Materials of the All-Russian Congress of Beam Diagnosticians), Moscow, 6–8 June 2007, рр. 165–166. (in Russ.)
9. Lindenbraten L.D., Korolyuk I.P. Meditsinskaya radiologiya i rentgenologiya (osnovy luchevoy diagnostiki i luchevoy terapii) (Medical Radiology and Roentgenology (Basis of Radiodiagnosis and Radiation Therapy)), Moscow, 2003. (in Russ.)
10. Certificate on the state registration of the computer programs 2013613849 RU, Polucheniye parametrov komp’yuternykh izobrazheniy (Obtaining Parameters of Computer Images), O.I. Vorona, S.L. Leonov, V.K. Konovalov, A.M. Shayduk, 17.04.2013. (in Russ.)
11. Klonowski W., Stepien R., Stepien P. Nonlinear biomedical physics, 2010, no. 7(4). DOI:10.1186/1753-4631-4-7 (in Russ.)
12. Ostanin S.A. Shayduk A.M. Zhurnal radioelektroniki, 2013, no. 2. (in Russ.)