Saratov JOURNAL of Medical and Scientific Research

Cells identification and counting in blood native state on the basis of digital microscopy.

Year: 2016, volume 12 Issue: №4 Pages: 549-555
Heading: Physiology and Pathophysiology Article type: Original article
Authors: Doubrovski V.A., Zabenkov I.V, Torbin SO, Tsareva O.E.
Organization: Saratov State Medical University

The research goal is to develop an algorithm for the processing of photo images of native blood samples to determine the concentration of erythrocytes, leukocytes and platelets without individual separate preparation of cell samples. Materials and Methods. The objects of investigation were the samples of the whole donated blood, diluted 400 times by saline. Special "photo templates", the effect of "highlighting" of leukocytes, which was detect by authors, and the resolution of platelets from leukocytes by the areas of their photo images were suggested for identification of the cells. Results. 80 photo images of native blood solutions were selected for computer processing, while the total number of cells counted was: erythrocytes — 4184, platelets — 292 and leukocytes — 84, total — 4560 blood cells. Comparison of the results achieved with ones obtained by "manual" account or by the device for formed elements counting Sysmex XT-400i gives satisfactory results. Conclusion. It is shown that the accuracy of counting of the native blood cells may be comparable with the accuracy of similar studies by means of smears. At the same time the proposed analysis of native blood simplifies greatly the samples preparation in comparison to smears, permits to move from the detection of blood cells ratios to the determination of their concentrations in the sample.

1. Steinkamp JA. Flow cytometry. Rev Sci Instrum 55 (1984); 9: 1375-1400
2. Tuchin VV, ed. Advanced Optical Flow Cytometry: Methods and Disease Diagnoses. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2011; 701 p.
3. Orfao A, Lacombe F, Ault K, et al. Flow cytometry: its applications in hematology. Haematologica 1995; 80: 69-81
4. Canellini G, Rubin O, Delobel J, et al. Red blood cell microparticles and blood group antigens: an analysis by flow cytometry. Blood Transfus 2012; 10 Suppl 2: 39-45
5. Vyas GN, et al. Simultaneous human ABO and Rh (D) blood typing or antibody screening by flow cytometry: United States Patent 5,776,711 July 7, 1998
6. Tatsumi N, Tsuda I, Inoue K. Trial ABO and Rh blood typing with an automated blood cell counter. Clin Lab Haemotol 1989; 11 (2): 123-30
7. Doubrovski VA, Dvoretski KN, Shcherbakova IV, et al. Laser space scanning in flow cytometry. Tsitologiya 1999; 41 (1): 104-108
8. Doubrovski VA, Ganilova YuA, Zabenkov IV R and G color components competition of RGB image decomposition as a criterion to register RBC agglutinates for blood group typing. J Biomed Opt 2014; 19(3): doi 10.1117/1. JBO.19.3.036012
9. Dyrnaev AV The process of erythrocytes' counting in the images of blood smears (options): Patent RU 2488821 C1. Published 27.07.2013. Bull. 21
10 Dyrnaev AV The method of erythrocytes' counting for the blood smear images. Scientific and Technical Bulletin of St. Petersburg State University of Information Technologies, Mechanics and Optics 2011; 76 (6): 18-23
11. Dyrnaev AV, Potapov AS. The combined red blood cells' counting in the images of blood smears. Scientific and Technical Bulletin of St. Petersburg State University of Information Technologies, Mechanics and Optics 2012; 77 (1): 20-24
12. Maitra М, Gupta RK, Mukherjee М. Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform. International Journal of Computer Applications 2012; 53(16): 18-22
13. Mahmood NH, Mansor MA. Red blood cells estimation using Hough transform technique. Signal & Image Processing: An International Journal (SIPIJ) 2012; 3 (2): 53-64
14. Mazalan SM, Mahmood NH, Razak MA. Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform. In: First International Conference on Artificial Intelligence, Modelling & Simulation, IEEE978-1-4799-3251-1/13, IEEE DOI 10.1109/AIMS.2013.59; p 285-289
15. Pandit A, Kolhar S, et al. Survey on Automatic RBC Detection and Counting. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2015; 4(1): 128-131
16. Taherisadr M, Nasirzonouzi M, et al. New Approch to Red Blood Cell Classification Using Morphological Image Processing. Shiraz E-Medical Journal 2013; 14 (1): 44-53
17. Alilou M, Kovalev V. Automatic object detection and segmentation of the histocytology images using reshapable agents, International. Journal of Research in Engineering and Technology 2014; 3 (4): 2321-7308
18. Cuevas E, Diaz M, Manzanares M, et al. An improved computer vision method for detecting white blood cells. In: Computational and Mathematical Methods in Medicine, 2013; art. no. 137392; p. 1-19
19. Hiremath PS, et al. Automated Identification and Classification of White Blood Cells (Leukocytes) in Digital Microscopic Images. In: Special Issue on "Recent Trends in Image Processing and Pattern Recognition" RTIPPER, 2010; p. 59-63
20. Sosnin DY, Falkov BF, Nenashev OY. Evaluation of cells' recognition accuracy for automated analysis system Vision He Blood. Urals Medical Journal 2012; (13): 1-7
21. Sable GS, et al. Counting of WBCs and RBCs from blood images using gray thresholding. International Journal of Research in Engineering and Technology 2014; 3 (4): 2321-7308
22. Doubrovski VA, Zabenkov IV, Torbin SO, et al. Determination of Platelet Aggregate Size in vitro Using Digital Microscopy. Biomedical Engineering 2013; 47 (3): 121-125.

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