Detection and Counting of Blood Cells in Blood Smear Image

Authors

  • K. Pradeep Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • C. Ganthimathi Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • K. Harini Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • N. Diddha Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajeat-2016.5.2.806

Keywords:

Hepatitis B, Pathological tests, Image Processing, Morphological operations

Abstract

This paper deals with an image processing technique used for detecting the blood cells in less time. The proposed technique also helps in counting and segregating the blood cells in blood smear image of different categories based on the form factor using various Morphological operations. Nowadays in Hospitals and clinical Laboratories the waiting time for getting their blood results and reports are more commonly 24 hours to 8 days in case of high severity diseases where the mortality rates are high. Doctors and technicians in healthcare sectors recommended that the patient’s waiting time should be as less as possible and the treatment should be started immediately for the high risk diseases like Hepatitis B. The major other factor affects patient in healthcare field is the more expensive pathological tests which sometimes leading to loss of patient’s life. The proposed technique gives improved accuracy in counting the number of blood cells in blood smear image in compare to manual counting in laboratories.

References

Gonzalez, "Digital Image Processing Using MATLAB," 2010.

Nasrul Humaimi Mahmood et al., "Red Blood Cells Estimation using Hough Transform technique," An International Journal (SIPIJ), vol. 3, no. 2, April 2012.

Pradeep. K., et al., "A Novel Approach for Prediction of Bulging in the type A Dissected Aorta Using MIMICS Tool," Asian Journal of Science and Applied Technology, vol. 4, no. 1, pp. 26-31, 2015.

Mythili A. et al., "Morphological Detection of Abnormal Cells in Blood Sample of Humans," International Journal of Scientific Research in Science, Engineering and Technology, vol. 2, issue 2, pp. 413-419, March-April 2016.

Selvaraj, Sriram, and Bommanna Raja Kanakaraj, "K-Means Clustering Based Segmentation of Lymphocytic Nuclei for Acute Lymphocytic Leukemia Detection," International Journal of Applied Engineering Research, vol. 9, no. 21, pp. 11423-11432, 2014.

Zainb Nayyar, "Blood cell detection and counting," International Journal of Applied Engineering Research and Development (IJAERD), vol. 4, issue 2, pp. 35-40, Apr 2014.

Bain, Barbara J., "Diagnosis from the blood smear," New England Journal of Medicine, vol. 353, no. 5, pp. 498-507, 2005.

Patil, Deepika N., and Uday P. Khot, "Image Processing Based Abnormal Blood Cells Detection."

Jagadeesh, S., E. Nagabhooshanam, and S. Venkatachalam, "Image processing based approach to cancer cell prediction in blood samples," ME&HWDS Int. J. Technol. Eng. Sci, vol. 1, no. 1, 2013.

Romero, Roberto, et al., "A comparative study of the diagnostic performance of amniotic fluid glucose, white blood cell count, interleukin-6, and gram stain in the detection of microbial invasion in patients with preterm premature rupture of membranes," American journal of obstetrics and gynecology, vol. 169, no. 4, pp. 839-851, 1993.

Sriram Selvaraj et al., "Naïve Bayesian Classifier for Acute lymphocytic leukemia detection," ARPN Journal of Engineering and Applied Sciences, vol. 10, issue 16, pp. 6888-6892.

N. Halim, M. A. Y. Mashor, and R. Hassan, "Automatic Blasts Counting for AcuteLeukemia Based on Blood Samples," International Journal of Research And Reviews in Computer Science, vol. 2, no. 4, August 2011.

Neha Sharma, Nishant Kinra, "Detection and counting of white blood cells in blood sample images by color based k-means clustering," IJEEE, vol. 1, issue 3, June 2014.

Siti Madihah Mazalan, Nasrul Humaimi Mahmood, and Mohd Azhar Abdul Razak, "Automated red blood cells counting In peripheral blood smear image using hough transform," 2013 IEEE, DOI: 10.1109/AIMS.2013.59.

S. L. Bhagavathi and S. Thomas Niba, "An automatic system for detecting and counting RBC and WBC using fuzzy logic," vol. 11, no. 11, June 2016.

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Published

18-07-2016

How to Cite

Pradeep, K., Ganthimathi, C., Harini, K., & Diddha, N. (2016). Detection and Counting of Blood Cells in Blood Smear Image. Asian Journal of Engineering and Applied Technology, 5(2), 1–5. https://doi.org/10.51983/ajeat-2016.5.2.806