Literature DB >> 18514244

Improving quantitation of malaria parasite burden with digital image analysis.

John Frean1.   

Abstract

Quantitation of malaria parasite burden has prognostic value as well as providing objective evidence of response to treatment or, potentially, to vaccination against malaria. Estimation of parasite load by microscopy is prone to inaccuracy and inconsistency. Digital image analysis is well suited to this application rather than to the more difficult task of malaria diagnosis and species identification. Preliminary work has shown the feasibility of using off-the-shelf hardware and software. Standardised banks of slides for comparing human and machine counts, cheaper imaging methods for laboratories with limited resources, and customisation of readily available image analysis software are proposed as priority needs.

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Year:  2008        PMID: 18514244     DOI: 10.1016/j.trstmh.2008.04.017

Source DB:  PubMed          Journal:  Trans R Soc Trop Med Hyg        ISSN: 0035-9203            Impact factor:   2.184


  5 in total

Review 1.  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

2.  Performance of a malaria microscopy image analysis slide reading device.

Authors:  William R Prescott; Robert G Jordan; Martin P Grobusch; Vernon M Chinchilli; Immo Kleinschmidt; Joseph Borovsky; Mark Plaskow; Miguel Torrez; Maximo Mico; Christopher Schwabe
Journal:  Malar J       Date:  2012-05-06       Impact factor: 2.979

3.  Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears.

Authors:  Charles Ma; Paul Harrison; Lina Wang; Ross L Coppel
Journal:  Malar J       Date:  2010-12-01       Impact factor: 2.979

4.  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

5.  An automatic device for detection and classification of malaria parasite species in thick blood film.

Authors:  Saowaluck Kaewkamnerd; Chairat Uthaipibull; Apichart Intarapanich; Montri Pannarut; Sastra Chaotheing; Sissades Tongsima
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

  5 in total

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