Recognition of Infected Erythrocytes by Inclusion Tree Representation and Parasitemia Estimation in Blood Smear Images

Authors

  • Doke Pranoti R.  Lecturer, Government Residence Womens Polytechnic, Tasgaon, Maharashtra, India
  • Doke Pooja R  Government Residence Womens Polytechnic, Tasgaon, Maharashtra, India

DOI:

https://doi.org//10.32628/IJSRST196149

Keywords:

Segmentation, Clumped Cells, Plasmodium Falciparum, Inclusion Tree Representation, Counting, Labeling

Abstract

Blood cells are composed of erythrocytes (red blood cells, RBCs), leukocytes (white blood cells, WBCs) and thrombocytes (platelets). Both WBC and RBC have fixed count in our body. If their count is less than the ideal count then it is an indication that our body is not healthy. Hence blood count helps in detecting many diseases in early stage.

According to World Health Organization about 3.2 billion people are at risk of malaria[2]. But, malaria is preventable and curable, if the patient is correctly diagnosed in early stage.

The proposed approach to diagnose malaria mainly consists of following steps:

  1. Preprocessing, Histogram and Segmentation
  2. Inclusion-Tree representation
  3. Splitting of clumped erythrocytes
  4. Counting and labeling
  5. Cell stage identification
  6. Feature extraction and classification

The algorithm is used to count malaria infected RBC in blood smear. A clump splitting method is used for precise RBC counting. Cell stage identification is performed in this approach by calculating Equivalent Circular Diameter. Quantification method improves overall performance in the determination of stages of infection such as ring, trophozoite and Schizont. Percentage of Parasitemia is calculate.

References

  1. R. C. Gonzalez and R. E. Woods, Digital Image Processing. 3rd ed.,Prentice Hall, 2007.
  2. World Malaria Report 2011, World Health Organization, Geneva, 2011.
  3. “A semi-automatic method for quantification and classification of erythrocytesinfected with malaria parasites in microscopic images” in 2009 Elsevier Journal of Biomedical Informatics 42 (2009) 296–307
  4. Pranati Rakshit, Kriti Bhowmik, “Detection of presence of Parasites in Human RBC in Case of diagnosing malaria using Image Processing “ in 2013, IEEE conference.
  5. www.mathworks.in/help/images/ref/regionprops.html
  6. https://in.mathworks.com/matlabcentral/fileexchange/28114-fast-edges-of-a-color-image--actual--color--not-converting-to-grayscale-/content/coloredges.m

International Journal of Scientific Research in Science and Technology

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Published

2019-02-28

Issue

Volume 6, Issue 1, January-February-2019

Section

Research Articles

License

Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Doke Pranoti R., Doke Pooja R, " Recognition of Infected Erythrocytes by Inclusion Tree Representation and Parasitemia Estimation in Blood Smear Images, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6, Issue 1, pp.308-312, January-February-2019. Available at doi : https://doi.org/10.32628/IJSRST196149