Multidisciplinary Journal of Science and Technology

DIGITAL IMAGE SEGMENTATION PROCEDURE AS AN EXAMPLE OF AN NP-PROBLEM

Valentin Lyubchenko

Department of Informatics, Kharkiv National University of Radio Electronics, Ukraine

Kostiantyn Veretelnyk

Simon Kuznets Kharkiv National University of Economics, Postgraduate student of the faculty Cyber security and information technologies, Ukraine

Polina Kots

Student of specialty “Printing and publishing”, IT faculty, Simon Kuznets Kharkiv National University of Economics, Ukraine

Vyacheslav Lyashenko

Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine

Keywords: Segmentation, Perception, Analysis, NP-problem, Quality, Metric, Digital image.

---

Abstract

Digital images are a source of additional information about the world around us. Such a source plays an important role in the process of medical diagnosis and research into human health. Digital imaging allows you to obtain the necessary information remotely without additional interference in human life. This can be done using various digital image processing and analysis techniques. However, these methods are typically an NP-problem. The paper discusses the procedure for segmenting medical digital images. The criteria are shown to achieve the required solution when segmenting an image as an NP-problem.

---

References

Jayaraman, S., Esakkirajan, S., & Veerakumar, T. (2009). Digital image processing (Vol. 7014). New Delhi: Tata McGraw Hill Education.

Herrera-Pereda, R., Crispi, A. T., Babin, D., Philips, W., & Costa, M. H. (2021). A review on digital image processing techniques for in-vivo confocal images of the cornea. Medical Image Analysis, 73, 102188.

Ni, T., Zhou, R., Gu, C., & Yang, Y. (2020). Measurement of concrete crack feature with android smartphone APP based on digital image processing techniques. Measurement, 150, 107093.

Castleman, K. R. (1996). Digital image processing. Prentice Hall Press.

McAndrew, A. (2016). A computational introduction to digital image processing (Vol. 2). Boca Raton: CRC Press.

Deineko, Zh., & et al.. (2021). Color space image as a factor in the choice of its processing technology. Abstracts of I International scientific-practical conference «Problems of modern science and practice» (September 21-24, 2021). Boston, USA, pp. 389-394.

Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General Methodology for Implementation of Image Normalization Procedure Using its Wavelet Transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

Kobylin, O., & Lyashenko, V. (2014). Comparison of standard image edge detection techniques and of method based on wavelet transform. International Journal, 2(8), 572-580.

Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

Hu, J., & Han, F. (2009). A pixel-based scrambling scheme for digital medical images protection. Journal of Network and Computer Applications, 32(4), 788-794.

Erickson, B. J. (2002). Irreversible compression of medical images. Journal of Digital Imaging, 15, 5-14.

Lyashenko, V. V., Babker, A. M. A. A., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.

Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

Mousavi, S. M. H., Victorovich, L. V., Ilanloo, A., & Mirinezhad, S. Y. (2022, November). Fatty Liver Level Recognition Using Particle Swarm optimization (PSO) Image Segmentation and Analysis. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 237-245). IEEE.

Boboyorov Sardor Uchqun o'g'li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Image Processing Techniques as a Tool for the Analysis of Liver Diseases. Journal of Universal Science Research, 1(8), 223–233.

Uchqun o‘g‘li, B. S., Nataliya, B., & Vyacheslav, L. (2023). Digital image of a blood smear as an object for research. Journal of Universal Science Research, 1(10), 517-525.

Uchqun o‘g‘li, B. S., Valentin, L., & Vyacheslav, L. (2023). Pre-processing of digital images to improve the efficiency of liver fat analysis. Multidisciplinary Journal of Science and Technology, 3(1), 107-114.

Uchqun o‘g‘li, B. S., Nataliya, B., & Vyacheslav, L. (2023). Digital image of a blood smear as an object for research. Journal of Universal Science Research, 1(10), 517-525.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural networks as a tool for pattern recognition of fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

Mousavi, S. M. H., Lyashenko, V., & Prasath, S. (2019). Analysis of a robust edge detection system in different color spaces using color and depth images. Компьютерная оптика, 43(4), 632-646.

Tahseen A. J. A., & et al.. (2023). Binarization Methods in Multimedia Systems when Recognizing License Plates of Cars. International Journal of Academic Engineering Research (IJAER), 7(2), 1-9.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour detection and allocation for cytological images using Wavelet analysis methodology. International Journal, 4(1), 85-94.

Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

Maksymova, S., Matarneh, R., Lyashenko, V. V., & Belova, N. V. (2017). Voice Control for an Industrial Robot as a Combination of Various Robotic Assembly Process Models. Journal of Computer and Communications, 5, 1-15.

Vasiurenko, O., Lyashenko, V., Baranova, V., & Deineko, Z. (2020). Spatial-Temporal Analysis the Dynamics of Changes on the Foreign Exchange Market: an Empirical Estimates from Ukraine. Journal of Asian Multicultural Research for Economy and Management Study, 1(2), 1-6.

Vasiurenko, O., & Lyashenko, V. (2020). Wavelet coherence as a tool for retrospective analysis of bank activities. Economy and forecasting, (2), 32-44.

Ahmad, M. A., Kuzemin, O., Lyashenko, V., & Ahmad, N. A. (2015). Microsituations as part of the formalization of avalanche climate to avalanche-riskiness and avalanche-safety classes in the emergency situations separation. International Journal, 3(4), 684-691.

Mustafa, S. K., Lyashenko, V., Ameer Ahamad, N., Rehan, M., & Ajmal, A. A. (2021). Some aspects of modeling in the study of COVID-19 data. International Journal of Pharmaceutical Research, 4124-4129.

Babker, A., & Lyashenko, V. (2018). Identification of megaloblastic anemia cells through the use of image processing techniques. Int J Clin Biomed Res, 4, 1-5.

Ahmad, M. A., Kots, G. P., & Lyashenko, V. V. (2015). Bank Lending Efficiency in the Real Sector of the Economy of Ukraine within the Period of 2011 to 2014 Years. Modern Economy, 6(12), 1209.

Patil, D. D., & Deore, S. G. (2013). Medical image segmentation: a review. International Journal of Computer Science and Mobile Computing, 2(1), 22-27.

Ramesh, K. K. D., Kumar, G. K., Swapna, K., Datta, D., & Rajest, S. S. (2021). A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7(27), e6-e6.

Salem, M. A. M., Atef, A., Salah, A., & Shams, M. (2017). Recent survey on medical image segmentation. In Handbook of Research on Machine Learning Innovations and Trends (pp. 424-464). IGI global.

Kleinberg, J., Papadimitriou, C., & Raghavan, P. (2004). Segmentation problems. Journal of the ACM (JACM), 51(2), 263-280.

Settouti, N., Saidi, M., Bechar, M. E. A., El Habib Daho, M., & Chikh, M. A. (2020). An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation. Pattern Analysis and Applications, 23, 1709-1726.

Wu, Q., & Castleman, K. R. (2023). Image segmentation. In Microscope Image Processing (pp. 119-152). Academic Press.

Wu, J., Li, X., Li, X., Ding, H., Tong, Y., & Tao, D. (2024). Towards robust referring image segmentation. IEEE Transactions on Image Processing.

Babker, A. M., Suliman, R. S., Elshaikh, R. H., Boboyorov, S., & Lyashenko, V. (2024). Sequence of Simple Digital Technologies for Detection of Platelets in Medical Images. Biomedical & Pharmacology Journal, 17(1), 141-152.