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Review Article

Year : 2018 | Volume: 4 | Issue: 2 | Pages: 5-28

Methodical Approaches of Image Analysis on Malarial Parasites Identification and Categorization in Thick Blood Smears-A Review

T Susitha1*, C Berin Jones2

http://dx.doi.org/doi:10.18831/djcse.in/12018021002

Corresponding author

T Susitha*

Senior assistant professor, Department of Microbiology, Kanyakumari Government Medical College.

  • 1. Senior assistant professor, Department of Microbiology, Kanyakumari Government Medical College.

Received on: 2018/02/07

Revised on: 2018/03/21

Accepted on: 2018/03/26

Published on: 2018/04/03

  • Methodical Approaches of Image Analysis on Malarial Parasites Identification and Categorization in Thick Blood Smears-A Review, T Susitha, C Berin Jones, 2018/04/03, Journal of Excellence in Computer Science and Engineering, 4(2), 5-28, http://dx.doi.org/10.18831/djcse.in/12018021002.

    Published on: 2018/04/03

Abstract

Clinical trials have started focusing on other plasmodium species too, contrasting to the emphasis on Plasmodium falciparum so far. This review article lists out certain major techniques in detection and classification of malarial parasites. Although the world has succeeded in finding out the devices that detect and classify the malaria parasites well in thin blood films, technology still lacks sufficient innovation in identifying and classifying these infectious parasites in thick blood films. In order to fill the gap, it is aimed to provide a brief study of a collection of methods and systems especially via image processing in which these concepts are prioritized. Thick blood smear is primarily focused that would let in knowing the percentage of infected red blood cells by identifying the malarial parasites. Real time in vivo optical imaging of infected cells based on automated computer vision provides possible insights about the plasmodium species that could be applied to treat and prevent malaria.

Keywords

Malaria parasites, Detection, Classification, Image processing, Thick blood smears, Automated computer vision.