Disease Diagnosis Using RBCs & WBCs Cell Structure by Image Processing

Authors(4) :-Prof. Hina Malik, Roopali Randiwe, Jyotsna Patankar, Priya Bhure

The human blood consists of the RBCs, WBCs, Platelets and Plasma. The complete blood count defines the state of health. Blood is a health indicator therefore segmentation and identification of blood cells is very important. Complete Blood Count (CBC) includes counting of all the cells which determines person’s health. The RBC and WBC count is very important to diagnose various diseases such as anemia, leukemia, tissue damage, etc. Old conventional method used in the hospital laboratories involves manual counting of blood cells using device called Hemocytometer and microscope. But this method extremely monotonous, laborious, time consuming, and leads to the inaccurate results due to human errors. Also there are some expensive machines like Analyzer, which are not affordable by every laboratory. The objective of this paper is to produce a survey on an image processing based system that can automatically detect and count the number of RBCs and WBCs in the blood sample image. Image Acquisition, Pre-Processing, Image Enhancement, Image Segmentation, Image Post-Processing and Counting algorithm these are six steps involved in an image processing algorithm. The objective of this research is to diagnosed the different diseases of the blood cells.

Authors and Affiliations

Prof. Hina Malik
Department of Electronics and Telecommunication, SRMCEW, Nagpur, Maharashtra, India
Roopali Randiwe
Department of Electronics and Telecommunication, SRMCEW, Nagpur, Maharashtra, India
Jyotsna Patankar
Department of Electronics and Telecommunication, SRMCEW, Nagpur, Maharashtra, India
Priya Bhure
Department of Electronics and Telecommunication, SRMCEW, Nagpur, Maharashtra, India

RBC, WBC, Platelets, Digital Image Processing, Morphology, Hough Transform.

  1. S. Kareem, R.C.S Morling and I. Kale, "A Novel Method to Count the Red Blood Cells in Thin Blood Films", IEEE International Symposium on circuits and systems, pp. 1021 – 1024, May – June 2011
  2. Yazan M.Alomari, SitiNorulHuda Sheikh Abdullah,RajaZaharatulAzma,andKhairuddinOmar1 "Automatic Detection and Quantification of  WBCs and RBCs Using Iterative Structured Circle Detection Algorithm", Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2014.
  3. EstiSuryani, Wiharto, and Nizomjon Polvonov "Identification  and Counting White Blood Cells and Red Blood Cells using Image Processing Case Study of Leukemia "International journal of Computer Science & Network Solutions  Jun.2014-Volume.
  4. Akshaya  P. Sahastrabuddhe1 "COUNTING OF RBC AND WBC USING IMAGE PROCESSING: A REVIEW ", IJRET: International Journal of Research in Engineering and Technology. eISSN: 2319-1163 | pISSN: 2321-7308
  5. Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis, and machine vision.Cengage Learning, 2014.
  6. SaicholSinsombulthong. DATA MINING.Bangkok, 2015.

Publication Details

Published in : Volume 3 | Issue 2 | January-February 2017
Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 120-123
Manuscript Number : NCAEAS2328
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Prof. Hina Malik, Roopali Randiwe, Jyotsna Patankar, Priya Bhure, " Disease Diagnosis Using RBCs & WBCs Cell Structure by Image Processing, International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.120-123, January-February-2017. Available at doi : 10.32628/NCAEAS2328
Journal URL : http://ijsrst.com/NCAEAS2328

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