Computer vision for microscopy diagnosis of malaria

F Boray Tek,Andrew G Dempster,Izzet Kale

doi:10.1186/1475-2875-8-153

F Boray Tek, Andrew G Dempster + Show 1 more

Open Access

https://doi.org/10.1186/1475-2875-8-153

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Journal: Malaria journal	Publication Date: Jul 13, 2009
Citations: 206	License type: CC BY 2.0

Affiliation: University of Westminster, UNSW Sydney

Abstract

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Highlights

Malaria is a serious infectious disease caused by a peripheral blood parasite of the genus Plasmodium
Diagnosis using a microscope requires special training and considerable expertise [4]. It has been shown in several field studies that manual microscopy is not a reliable screening method when performed by nonexperts due to lack of training especially in the rural areas where malaria is endemic [5,6,7]
This study provides an overview of computer vision studies of malaria diagnosis and intends to fill a gap in this area by doing so

Summary

Introduction

Malaria is a serious infectious disease caused by a peripheral blood parasite of the genus Plasmodium. There are newer techniques [2], manual microscopy for the examination of blood smears [3] (invented in the late 19th century), is currently "the gold standard" for malaria diagnosis. Diagnosis using a microscope requires special training and considerable expertise [4]. It has been shown in several field studies that manual microscopy is not a reliable screening method when performed by nonexperts due to lack of training especially in the rural areas where malaria is endemic [5,6,7]. An automated system aims at performing this task without human intervention and to provide an objective, reliable, and efficient tool to do so

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