Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)


For citation:

Doubrovski V. A., Torbin S. O. Leukocytes’ «Highlighting» Effect and its Application to Identify Blood Cells by Digital Microscopy Method. Izvestiya of Saratov University. Physics , 2017, vol. 17, iss. 3, pp. 191-200. DOI: 10.18500/1817-3020-2017-17-3-191-200

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text:
(downloads: 239)
Language: 
Russian
UDC: 
004.932

Leukocytes’ «Highlighting» Effect and its Application to Identify Blood Cells by Digital Microscopy Method

Autors: 
Doubrovski Valerii Aleksandrovich, Saratov State Medical University named after V. I. Razumovsky
Torbin Stanislav Olegovich, Saratov State Medical University named after V. I. Razumovsky
Abstract: 

Objective: To find a way of identifying and counting of leukocytes in a native blood sample. Materials and equipments: Whole donor blood sample, digital microscop. Methods and approaches: The development of a method of leukocytes’ identification and counting for native blood samples was carried out on the basis of digital microscopy method. Main results: Leukocytes’ “highlighting” effect in a native blood sample was revealed experimentally by digital microscope examining. The effect is that when a microscope lens, initially focused on the object, is removed away from the leukocyte, its image becomes transformed and cell’s image brightness increases. At the same time, this phenomenon is not observed in respect to erythrocytes and platelets, what makes it possible to distinguish leukocyte from other types of blood cells during their counting. The effect revealed experimentally is not observed on any type of blood cells including leucocytes for the case of smears. It has been shown experimentally that the effect observed is based on the “lens” mechanism. Statistical studies were carried out on a variety of native blood cells. It is shown that the application of leukocytes’ “highlighting” effect gives a significant increase in the accuracy of counting of these type of cells when native blood formula is under analysis by digital microscopy method.

Reference: 
  1. Steinkamp J. A. Flow cytometry. Rev. Sci. Instrum., 1984, vol. 55, no. 9, pp. 1375–1400.
  2. Tuchin V. V., Galanzha E. I., Zharov V. P. In vivo Image Flow Cytometry. In: Advanced Optical Flow Cytometry: Methods and Disease Diagnoses, First Edition. Ed. by Valery V. Tuchin. 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA. 387-431. DOI: https://doi.org/10.1002/9783527634286.ch14
  3. Orfao A., Ruiz-Arguelles A., Lacombe F., Ault K., Basso G., Danova M. Flow cytometry: its applications in hematology. Haematologica, 1995, vol. 80, iss. 2, pp. 69–81.
  4. Canellini G., Rubin O., Delobel J., Crettaz D., Lion N., Tissot J. Red blood cell microparticles and blood group antigens: an analysis by fl ow cytometry. Blood Transfus., 2012, vol. 10, pp. 39–45.
  5. Vyas G. N. Simultaneous human AB0 and RH(D) blood typing or antibody screening by fl ow cytometry. US Patent, no. 5776711, published on July 7, 1998.
  6. Tatsumi N., Tsuda I., Inoue K. Trial AB0 and Rh blood typing with an automated blood cell counter. Clin. Lab. Haemotol., 1989, vol. 11, iss. 2, pp. 123–130.
  7. Doubrovski V. A., Dvoretski K. N., Shchebakova I. V., Balaev A. E., Kirichuk V. F. Lazernoe prostranstvennoe skanirovanie v protochnoj citometrii [Laser space scanning in fl ow cytometry]. Сytology, 1999, vol. 41, iss. 1, pp. 104–108 (in Russian).
  8. Doubrovski V. A., Ganilova Yu. A., Zabenkov I. V. R and G color components competition of RGB image decomposition as a criterion to register RBС agglutinates for blood group typing. J. Biomed. Opt., 2014, vol. 19, iss. 3. DOI: https://doi.org/10.1117/1.JBO.19.3.036012
  9. Dyrnaev A. V. Sposob podscheta ehritrocitov na izobrazheniyah mazkov krovi [Method of counting red blood cells on images of blood smears] Pat. 2488821 MPK G01N 33.48; zayavitel’ i patentoobladatel’ Federal’noe gosudarstvennoe byudzhetnoe obrazovatel’noe uchrezhdenie vysshego professional’nogo obrazovaniya “SanktPeterburgskij nacional’nyj issledovatel’skij universitet informacionnyh tekhnologij, mekhaniki i optiki” – 2011152334/15, zayavl. 21.12.2011, opubl. 27.07.2013, Byul. № 21.
  10. Dyrnaev A. V. Metod podscheta ehritrocitov na izobrazheniyah mazkov krovi [The method of erythrocytes counting for the blood smear images]. Scientifi c and Technical Herald of St. Petersburg State University of Information Technologies, Mechanics and Optics, 2011, vol. 76, iss. 6, pp. 18– 23 (in Russian).
  11. Dyrnaev A. V., Potapov A. S. The combined red blood cells’ counting in the images of blood smears. Scientifi c and Technical Herald of St. Petersburg State University of Information Technologies, Mechanics and Optics, 2012, vol. 77, iss. 1, pp. 20–24.
  12. Maitra M., Gupta R. K., Mukherjee M. Detection and Counting of Red Blood Cells in blood cell Images using hough transform. International Journal of Computer Applications, 2012, vol. 53, iss. 16, pp. 18–22.
  13. Nasrul Humaimi Mahmood, Muhammad Asraf Mansor. Red blood cells estimation using Hough transform technique, Signal & Image Processing: An International Journal (SIPIJ). 2012, vol. 3, iss. 2, pp. 53–62.
  14. Siti Madihah Mazalan, Nasrul Humaimi Mahmood. Mohd Azhar Abdul Razak. Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform. In: First International Conference on Artifi cial Intelligence, Modelling & Simulation. Biosciences and Medical Engineering Universiti Teknologi Malaysia Johor, Malaysia, 2013, pp. 320–324. DOI: https://doi.org/10.1109/AIMS.2013.59
  15. Mahmood N. H., Mansor M. A. Red blood cells estimation using Hough transform technique. Signal & Image Processing: An International Journal (SIPIJ), 2012, vol. 3, iss. 2, pp. 53–64. DOI: https://doi.org/10.5121/sipij.2012.3204
  16. Pandit A., Kolhar S., Patil P., Survey on Automatic RBC Detection and Counting. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2015, vol. 4, iss. 1, pp. 128–131. DOI: https://doi.org/10.15662/ijareeie.2015.0401012
  17. Alilou M., Kovalev V. Automatic object detection and segmentation of the histocytology images using reshapable agents. Image Anal Stereol, 2013. Vol. 32, pp. 89–99. DOI: https://doi.org/10.5566/ias.V.32
  18. Cuevas E., Diaz M., Manzanares M., Zaldivar D., Perez M. An improved computer vision method for detecting white blood cells. Computational and Mathematical Methods in Medicine, 2013, art. no. 137392, pp. 1–19. DOI: https://doi.org/10.1155/2013/137392
  19. Hiremath P. S., Bannigidad P., Geeta S. Automated Identification and Classification of White Blood Cells (Leukocytes) in Digital Microscopic Images. IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition” RTIPPER, 2010, pp. 59–63.
  20. Sosnin D. Yu., Falkov B. F., Nenasheva O. Yu. Ocenka pravil’nosti raspoznavaniya kletok sistemoj avtomatizirovannogo analiza krovi [The estimation of the correctness of cells identifi cation by the automated blood cells counting system Vision He]. Ural Med. J., 2012, no. 13, pp. 1–7 (in Russian).
  21. Patil P. R., Sable G. S., Anandgaonkar G. Counting of WBCs and RBCs from blood images using gray thresholding. International Journal of Research in Engineering and Technology, 2014, vol. 3, iss. 4, pp. 391–395.
  22. Torbin S. O., Doubrovski V. A., Zabenkov I. V., Tsareva O. E. The counting of native blood cells by digital microscopy. Saratov Fall Meeting 2016: Optical Technologies in Biophysics and Medicine XVIII. Ed. by Elina A. Genina, Valery V. Tuchin, Proc. of SPIE Vol. 10336, 103360A (9 pp.). DOI: https://doi.org/10.1117/12.2268575
  23. Doubrovski V. A., Zabenkov I. V., Torbin S. O., Eremin V. I., Tsareva O. E. Determination of Platelet Aggregate Size in vitro Using Digital Microscopy. Biomedical Engineering, 2013, vol. 3, pp. 10–13.
Краткое содержание:
(downloads: 117)