Non-Invasive Method to Estimate Red Blood Cell in Blood
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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.
KeywordsRed blood cells Microcontroller Phototransistor red LED Sensors Voltage
Compliance with ethical standards
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 was obtained from all individual participants included in the study.
- 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_13Google 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
- 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.CrossRefGoogle 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
- 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
- 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
- 19.Automated Cell Counters & Viability Analyzers, https://chemometec.com/automated-cell-counters/, Accessed 31 July 2019
- 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