Skip to main content
SpringerLink
Log in

Non-Invasive Method to Estimate Red Blood Cell in Blood

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This work proposes a non-invasive method to estimate the number of red blood cells in the blood. To achieve the development of this research, first, a photosensitive device was designed, which is formed by a phototransistor with a transparent casing allowing the red light coming from a red LED to penetrate the sensor. This means, that when the intensity of the light varies, the amount of current flowing through the sensor also changes. In consequence, this variation in electric current causes a variation on the voltage drop across the connections of a resistor, which is read by a microcontroller that calculates the number of red blood cells. Second, some formulas were established to represent the relationship between the extreme points of a data set obtained during a sampling process. Finally, to verify the device operation, a sampling process was performed in volunteer patients (range 18–84 years) with venous blood samples run on a laboratory hematology analyzer, a total 68 measurements were made to people of different ages and genders, of which 34 are females and 34 are males.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nowrousian MR (2008) Definition, classification and characterization of anemia in cancer. In: Nowrousian M.R. (eds) Recombinant Human Erythropoietin (rhEPO) in Clinical Oncology. pp 117–148, Springer, Vienna. 10.1007/978-3-211-69459-6_13

  2. Verso, M. L., The evolution of blood-counting techniques. Med Hist 8(2):149–158, 1964. https://doi.org/10.1017/s0025727300029392.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Leukemia & Lymphoma Society Homepage, https://www.lls.org/, last accessed 2019/05/20.

  4. Bhamare, M. G., and Patil, D. S., Automatic blood cell analysis by using digital image processing: a preliminary study. Int J Eng Res Tech 2(9):3137–3141, 2013.

    Google Scholar 

  5. Dvanesh VD, Lakshmi PS, Reddy K, Vasavi AS (2018) Blood Cell Count using Digital Image Processing. In: Proc. IEEE Int Con on Cur Tren tow Conv Tech, pp 1–7 IEEE, https://doi.org/10.1109/ICCTCT.2018.8550999

  6. Mazalan SM, Mahmood NH, Razak MAA (2013) Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform. In Proc. IEEE Int Conf on Art Int, Mod and Sim, pp 320–324, IEEE, Kota Kinabalu. https://doi.org/10.1109/AIMS.2013.59

  7. Venkatalakshmi B, Thilagavathi K (2013) Automatic red blood cell counting using hough transform. In Proc. IEEE Conf. on Inf Comm Tech, pp 267–271, IEEE, Thuckalay, Tamil Nadu, India. https://doi.org/10.1109/CICT.2013.6558103

  8. Maitra, M., Gupta, R. K., and Mukherjee, M., Detection and counting of red blood cells in blood cell images using Hough transform. Int J Comp Appl 53(16):13–17, 2012. https://doi.org/10.5120/8505-2274.

    Article  Google Scholar 

  9. Mogra, M., Bansel, A., and Srivastava, V., Comparative analysis of extraction and detection of RBCs and WBCs using Hough transform and k–means clustering algorithm. Int J Eng Res Gen Sci. 2(5):670–674, 2014.

    Google Scholar 

  10. Kolhatkar D, Wankhade N (2016) Detection and counting of blood cells using image segmentation: A review. In Proc. IEEE Worl Conf on Fut Tren in Res and Inno for Soc Wel, pp 1–5, IEEE, Coimbatore. https://doi.org/10.1109/STARTUP.2016.7583931

  11. González-Betancourt, A., Rodríguez-Ribalta, P., Meneses-Marcel, A., Sifontes-Rodríguez, S., Lorenzo-Ginori, J. V., and Orozco-Morales, R., Automated marker identification using the Radon transform for watershed segmentation. IET Ima Proc 11(3):183–189, 2017. https://doi.org/10.1049/iet-ipr.2016.0525.

    Article  Google Scholar 

  12. Christy N, Annalatha M (2018) Computer Aided System for Human Blood Cell Identification, Classification and Counting. In Proc. IEEE Int Conf on Bio, Ima and Inst, pp 206–212, IEEE, Chennai, India. https://doi.org/10.1109/ICBSII.2018.8524636

  13. Acharya, V., and Kumar, P., Identification and red blood cell automated counting from blood smear images using computer-aided system. Med Biol Eng Comput 56(3):483–489, 2018. https://doi.org/10.1007/s11517-017-1708-9.

    Article  PubMed  Google Scholar 

  14. Acharya V, Kumar P (2017) Identification and red blood cell classification using computer aided system to diagnose blood disorders. In Proc. Int Conf on Adv in Comp, Comm and Inf, pp 2098–2104, Udupi. https://doi.org/10.1109/ICACCI.2017.8126155

  15. Pooja, G. S., Patil, R., and Anandgaonkar, G., Counting of wbcs and rbcs from blood images using gray thresholding. Int J Res in Eng and Tech 3(4):391–395, 2014. https://doi.org/10.15623/ijret.2014.0304071.

    Article  Google Scholar 

  16. Dvanesh VD, Lakshmi PS, Reddy K, Vasavi AS (2018) Blood Cell Count using Digital Image Processing. In: Proc. IEEE Int Con on Curr Tren tow Con Tech, pp 1–7, IEEE Press, Coimbatore. https://doi.org/10.1109/ICCTCT.2018.8550999

  17. Thejashwini, M., and Padma, M. C., Counting of RBC’s and WBC’s Using Image Processing Technique. Int J Rec Inn Tren Comp Comm 3(5):2948–2953, 2015.

    Google Scholar 

  18. Math, M. V., Kattimani, Y. R., Khadkikar, R. M., Patel, S. M., Shanti, V., Inamdar, R. S., Kaul, S. K., and Puri, C. P., Red Blood Cell Count: Brief History and New Method. J Medical Sciences 3(3):116–119, 2016. https://doi.org/10.5005/jp-journals-10036-1104.

    Article  Google Scholar 

  19. Automated Cell Counters & Viability Analyzers, https://chemometec.com/automated-cell-counters/, Accessed 31 July 2019

  20. Chhabra, G., Automated hematology analyzers: Recent trends and applications. J Lab Physicians. 10(1):15–16, 2018. https://doi.org/10.4103/JLP.JLP_124_17.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Simran, K., and Mohammad, S. A., Noninvasive Blood Cell Count of Flowing Blood Cells Using Surface Charge of Blood Cells Current Practice and Future Prospects. Curr Trends Biomedical Eng & Biosci 6(4):555695, 2019. https://doi.org/10.19080/CTBEB.2017.06.555695.

    Article  Google Scholar 

  22. Barker, S. J., Shander, A., and Ramsay, M. A., Continuous Noninvasive Hemoglobin Monitoring: A Measured Response to a Critical Review. Anesth Analg. 122(2):565–572, 2016. https://doi.org/10.1213/ANE.0000000000000605.

    Article  PubMed  Google Scholar 

  23. Segman, Y. J., and Sheiman, E., Post marketing study of hemodynamic and hematological noninvasive readings in a blood bank. SAGE Open Med 6:2050312118796065, 2018. https://doi.org/10.1213/ANE.0000000000000605.

    Article  PubMed  Google Scholar 

  24. Pathak, M., Rawat, R., Singh, S., and Thakur, R., Fire figthing robot remotely controlled by android application. Int J of Sci Eng and Res 5(5):106–109, 2017.

    Google Scholar 

  25. Iskander, D., and Bain, B. J., Overview. In: Lammert, E., Zeeb, M. (Eds), Metabolism of Human Diseases. Vienna: Springer, 2014, 281–287.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Autónoma de Sinaloa, Mazatlán, Mexico

    Yadira Quiñonez

  2. Instituto Tecnológico de Mazatlán, Mazaltán, Mexico

    Said Almeraya, Selin Almeraya & Jorge Reyna

  3. Centro de Investigación en Matemáticas, Zacatecas, Mexico

    Jezreel Mejía

Authors
  1. Yadira Quiñonez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said Almeraya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Selin Almeraya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Reyna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jezreel Mejía
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yadira Quiñonez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants

Patient measurements were collected in accordance with the code of conduct of research with human material in the Mexico. The sampling process was made at the Dr. Martiniano Carvajal Hospital facilities in Sinaloa, Mexico. All subjects gave written informed consent.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quiñonez, Y., Almeraya, S., Almeraya, S. et al. Non-Invasive Method to Estimate Red Blood Cell in Blood. J Med Syst 43, 316 (2019). https://doi.org/10.1007/s10916-019-1447-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1447-6

Keywords

Search

Navigation