Estimation of lung standing size with the application of computer vision algorithms
PDF (Українська)

Keywords

computer vision
spiral computed tomography
lungs
foreign bodies

How to Cite

Bunin, Y., Vakulik, E., Mikhaylusov, R., Negoduyko, V., Smelyakov, K., & Yasinsky, O. (2020). Estimation of lung standing size with the application of computer vision algorithms. Experimental and Clinical Medicine, 89(4), 87-94. https://doi.org/10.35339/ekm.2020.89.04.13

Abstract

Evaluation of spiral computed tomography data is important to improve the diagnosis of gunshot wounds and the development of further surgical tactics. The aim of the work is to improve the results of the diagnosis of foreign bodies in the lungs by using computer vision algorithms. Image gradation correction, interval segmentation, threshold segmentation, three-dimensional wave method, principal components method are used as a computer vision device. The use of computer vision algorithm allows to clearly determine the size of the foreign body of the lung with an error of 6.8 to 7.2%, which is important for in-depth diagnosis and development of further surgical tactics. Computed vision techniques increase the detail of foreign bodies in the lungs and have significant prospects for the use of spiral computed tomography for in-depth data processing.

Keywords: computer vision, spiral computed tomography, lungs, foreign bodies.

https://doi.org/10.35339/ekm.2020.89.04.13
PDF (Українська)

References

Ya.L. Zarutsky, V.Ya. Biliy (Eds.). (2018). Voyenno-polova khirurhiya [Military field surgery]. Kyiv: Phoenix, 552 p. [in Ukrainian].

Tsymbalyuk V.I. (Eds.). (2020). Vohnepalni poranennya myakykh tkanyn (dosvid ATO/OOS) [Gunshot wounds of soft tissues (experience of anti-terrorist operation / environmental protection)]. Kharkiv: Collegium, 400 p [in Ukrainian].

Gonzalez R.C., Richard E. (2018). Woods Digital Image Processing, 4th edition Pearson/Prentice Hall. New York, 1168p.

Smelyakov K.S. (2012) Modeli i metody segmentatsii izobrazheniy obyektov neregulyarnogo vida dlya avtonomnykh sistem tekhnicheskogo zreniya [Models and methods of image segmentation of irregular objects for autonomous systems of technical vision]. Doctor’s theis’s. Kharkov [in Russian].

Obucheniye bez uchitelya: PCA i klasterizatsiya. Lektsii i uchebnik po «Mashinnomu obucheniyu». Kurs 7[Unsupervised Learning: PCA and Clustering. Lectures and textbook on "Machine Learning". Course 7]. intellect.icu. Retrieved from https://intellect.icu/obuchenie-bez-uchitelya-pca-i-klasterizatsiya-7928 [in Russian].

Soifer V.A. (2003). Metody kompyuternoy obrabotki izobrazheniy [Computer image processing methods]. Moscow: Fizmatlit, 784 p. [in Russian].

Forsyth David A. (2015). Jean Ponce Computer Vision: A Modern Approach (2d ed.). Pearson Education Limited, 792p.

Smelyakov K., Chupryna A., Hvozdiev M., Sandrkin D., Martovytskyi V. (2019). Comparative efficiency analysis of gradational correction models of highly lighted image: 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) (8-11 Oct. 2019). (рр. 703–708). Kyiv, Ukraine.

Smelyakov K., Chupryna A., Hvozdiev M., Sandrkin D. (2019). Gradational Correction Models Efficiency Analysis of Low-Light Digital Image. 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), (25-25 April 2019). (рр. 34–39). Vilnius, Lithuania.