Advertisement

Automatic Segmentation of Malaria Affected Erythrocyte in Thin Blood Films

  • Komal B. RodeEmail author
  • Sangita D. Bharkad
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In today’s world, a highly précised diagnostic method needs to be improved for management of feverish sickness and ensure that medicines are prescribed when necessary. The proposed algorithm is applied to giemsa-stained thin blood films. Using triangle’s thresholding technique, the erythrocytes are segmented, HSV color space based feature extraction is applied on the segmented erythrocytes. Features extracted are given to SVM classifier to identify whether the query sample is affected with a parasite or a normal sample. The performance of this algorithm is evaluated on the database collected from CDC website and 20 samples taken manually from a local hospital.

Keywords

P. falciparum P. vivax Triangle’s thresholding SVM classifier RBC’s (erythrocytes) 

Notes

Acknowledgements

We are heartily grateful to the Medicare hospital, Ahmednagar for providing us the blood slides images which lead to the implementation of our project and some images were taken from the CDC website.

References

  1. 1.
    World Malaria Report: World Health Organization (2017)Google Scholar
  2. 2.
    Wassmer E (2017) Grau: Severe Malaria: What’s new on the pathogenesis front? Elsevier Int J Parasitol 47:145–152CrossRefGoogle Scholar
  3. 3.
    Azikiwe, Ifezulike, Siminialayi, Amazu, Enye, Nwakwunite (2012) A comparative laboratory diagnosis of malaria: microscopy versus rapid diagnostic test kits. Elsevier Asian Pac J Trop Biomed 4:307–310(2012)CrossRefGoogle Scholar
  4. 4.
    Nugroho HA, son of Ali Akbar, Elsa Herdiana Murhandar E (2015) Feature extraction and classification for detection of malaria parasites in a thin blood smear. In: Proceeding of IEEE conference on information technology, computer, and electrical engineering. pp 197–201Google Scholar
  5. 5.
    Yashasvi, Shah, Clarke, Almugairi A, Muehlenbachs A (2011) Automated and unsupervised detection of malarial parasites in microscopic images. Malar J 1–10Google Scholar
  6. 6.
    Gloria, Gonzalez, Romero (2009) A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Elsevier J Biomed Inf 42:296–307Google Scholar
  7. 7.
    Boray, Dempster, Kale (2009) Computer vision for microscopy diagnosis of malaria. Malar J 9:1–12Google Scholar
  8. 8.
    Minh-Tam Le, Bretschneider, Kuss, Preiser (2008) A novel semi-automatic image processing approach to determine Plasmodium falciparum Parasitemia in Giemsa-Stained thin blood smears. In: Research article BMC cell biology, pp 1–12Google Scholar
  9. 9.
    Kareem S, Morling RCS, Kale I (2011) A novel method to count the RBCs in thin blood films. In: Proceedings of IEEE conference on circuits and systems, pp 1021–1024Google Scholar
  10. 10.
    Kareem S, Morling RCS, Kale I (2012) Automated malaria parasites detection in thin blood films—a hybrid illumination and color constancy insensitive, morphological approach. In: Proceedings of IEEE conference on circuits and systems, pp 240–243Google Scholar
  11. 11.
    Sadeghian, Seman Z, Ramli AR, Kahar BHA, M-Iqbal Saripan (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing.11:196–206Google Scholar
  12. 12.
    https://www.cdc.gov (centre for disease control and prevention)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronics and TelecommunicationGovernment College of EngineeringAurangabadIndia

Personalised recommendations