Specifics of Using Image Processing Techniques for Blood Smear Analysis

  • Vyacheslav Lyashenko Department of Informatics, Kharkiv National University of Radio Electronics, Ukrainе
  • Tetiana Sinelnikova Department of Informatics, Kharkiv National University of Radio Electronics, Ukrainе
  • Oleksandr Zeleniy Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukrainе
  • Asaad Mohammed Ahmed Babker Department of Hematology, College of Medical Laboratory Sciences, University of Science and Technology, Omdurman, Sudan
Keywords: Blood Smear, Erythrocytes, Leukocytes, Cell Nucleus, Image Processing

Abstract

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.

References

Acharya, V., & Kumar, P. (2018). Identification and red blood cell automated counting from blood smear images using computer-aided system. Medical & biological engineering & computing, 56(3), 483-489.
Andrade, A. R., Vogado, L. H., de MS Veras, R., Silva, R. R., Araujo, F. H., & Medeiros, F. N. (2019). Recent computational methods for white blood cell nuclei segmentation: A comparative study. Computer Methods and Programs in Biomedicine, 173, 1-14.
Babker, A. M., & Lyashenko, V. (2020). Wavelet Analysis as Research Tool Image Cytological Preparations. J Clin Exp Pathol, 10, 382.
Dey, N., Ashour, A. S., Ashour, A. S., & Singh, A. (2015). Digital analysis of microscopic images in medicine. Journal of Advanced Microscopy Research, 10(1), 1-13.
Grill, A., Kiouptsi, K., Karwot, C., Jurk, K., & Reinhardt, C. (2020). Evaluation of blood collection methods and anticoagulants for platelet function analyses on C57BL/6J laboratory mice. Platelets, 31(8), 981-988.
Hegde, R. B., Prasad, K., Hebbar, H., & Singh, B. M. K. (2019). Development of a robust algorithm for detection of nuclei of white blood cells in peripheral blood smear images. Multimedia Tools and Applications, 78(13), 17879-17898.
Huang, D. C., Hung, K. D., & Chan, Y. K. (2012). A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. Journal of Systems and Software, 85(9), 2104-2118.
Li, Y., Xing, C., Wei, M., Wu, H., Hu, X., Li, S., ... & Li, Z. (2019). Combining red blood cell distribution width (RDW-CV) and CEA predict poor prognosis for survival outcomes in colorectal Cancer. Journal of Cancer, 10(5), 1162.
Lyashenko, V., Babker, A. M. A. A., & Kobylin, O. (2016). Using the methodology of wavelet analysis for processing images of cytology preparations. National Journal of Medical Research, 6(1). 98-102.
Lyashenko, V., Matarneh, R. & Kobylin, O. (2016). Contrast Modification as a Tool to Study the Structure of Blood Components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5, 150-160.
Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 665-670.
Safuan, S. N. M., Tomari, M. R. M., & Zakaria, W. N. W. (2018). White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods. Measurement, 116, 543-555.
Sapna, S., & Renuka, A. (2017). Techniques for segmentation and classification of leukocytes in blood smear images-a review. In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 1-5.
Tomari, R., Zakaria, W. N. W., Jamil, M. M. A., Nor, F. M., & Fuad, N. F. N. (2014). Computer aided system for red blood cell classification in blood smear image. Procedia Computer Science, 42, 206-213.
Published
2020-11-07