Literature DB >> 16837087

MalariaCount: an image analysis-based program for the accurate determination of parasitemia.

Selena W S Sio1, Weiling Sun, Saravana Kumar, Wong Zeng Bin, Soon Shan Tan, Sim Heng Ong, Haruhisa Kikuchi, Yoshiteru Oshima, Kevin S W Tan.   

Abstract

Malaria is a serious global health problem and rapid, precise determination of parasitemia is necessary for malaria research and in clinical settings. Manual counting by light microscopy is the most widely used technique for parasitemia determination but it is a time-consuming and laborious process. The aim of our study was to develop an automated image analysis-based system for the rapid and accurate determination of parasitemia. We have developed, for the first time, a software, MalariaCount, that automatically generates parasitemias from images of Giemsa-stained blood smears. The potential application and robustness of MalariaCount was tested in normal and drug-treated in vitro cultures of Plasmodium falciparum. The results showed a tight correlation between MalariaCount and manual count parasitemia values. These findings suggest that MalariaCount can potentially be used as a tool to provide rapid and accurate determination of parasitemia in research laboratories where frequent, large-scale, efficient determination of parasitemia is required.

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Year:  2006        PMID: 16837087     DOI: 10.1016/j.mimet.2006.05.017

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  19 in total

1.  Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.

Authors:  Keerthana Prasad; Jan Winter; Udayakrishna M Bhat; Raviraja V Acharya; Gopalakrishna K Prabhu
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

Review 2.  Image analysis and machine learning for detecting malaria.

Authors:  Mahdieh Poostchi; Kamolrat Silamut; Richard J Maude; Stefan Jaeger; George Thoma
Journal:  Transl Res       Date:  2018-01-12       Impact factor: 7.012

3.  Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples.

Authors:  Pramit Ghosh; Debotosh Bhattacharjee; Mita Nasipuri
Journal:  J Healthc Inform Res       Date:  2017-11-06

4.  Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation.

Authors:  Meng-Hsiun Tsai; Shyr-Shen Yu; Yung-Kuan Chan; Chun-Chu Jen
Journal:  J Med Syst       Date:  2015-08-20       Impact factor: 4.460

Review 5.  Computer vision for microscopy diagnosis of malaria.

Authors:  F Boray Tek; Andrew G Dempster; Izzet Kale
Journal:  Malar J       Date:  2009-07-13       Impact factor: 2.979

6.  VersaCount: customizable manual tally software for cell counting.

Authors:  Charles C Kim; Joseph L Derisi
Journal:  Source Code Biol Med       Date:  2010-01-13

7.  The evaluation of a semiautomated computer method to determine the effects of DMSO on Giardia lamblia-intestinal cell interaction.

Authors:  A P R Gadelha; R Travassos; L H Monteiro-Leal
Journal:  Parasitol Res       Date:  2007-07-22       Impact factor: 2.289

8.  Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

Authors:  Maitreya Maity; Dhiraj Dhane; Tushar Mungle; A K Maiti; Chandan Chakraborty
Journal:  J Med Syst       Date:  2017-10-26       Impact factor: 4.460

9.  Reliable enumeration of malaria parasites in thick blood films using digital image analysis.

Authors:  John A Frean
Journal:  Malar J       Date:  2009-09-23       Impact factor: 2.979

10.  Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.

Authors:  Miguel Angel Luengo-Oroz; Asier Arranz; John Frean
Journal:  J Med Internet Res       Date:  2012-11-29       Impact factor: 5.428

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