High-quality images for malaria diagnosis

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Automated digital imaging of thick blood films could expedite the identification of malaria and allow for more accurate treatment
 
Detection and classification of malaria parasite species in blood samples is time-consuming, involving highly skilled technicians using microscopes. Identifying parasites using automated methods could therefore help technicians to diagnose malaria more quickly and accurately.
 
Now, Sissades Tongsima and colleagues at the National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand, together with researchers at the National Electronics and Computer Technology Center (NECTEC) have developed a fully automated method for detecting and categorizing malaria parasite species using digital imaging.
 automatic device
Fig. 1: Automated equipment allows a digital camera connected to a computer to take high-definition images of malaria parasites in thick blood films. The high-quality images allow detection of structural differences between parasites — such as P. falciparum and P. vivax seen above — leading to quick species identification.
© 2013 Sissades Tongsima, BIOTEC

“The type of malaria-causing parasite present in a blood sample, and the developmental stage of the parasite, must be accurately determined in order to prescribe appropriate medication,” explains Tongsima. “Automated techniques have been established for thin blood films, but these hold fewer parasites and can yield misleading results.”

Tongsima and colleagues developed a method for taking high-definition images of thick blood films using a digital camera mounted on existing microscope equipment (Fig. 1).

“We devised a motorized unit for controlling the actions of the microscope stage and movement and autofocus of the lenses,” explains Tongsima. “The resulting images are processed using image analysis software via a computer linked to the digital camera.”

The team’s image-processing techniques allow rapid classification of separate species by revealing the developmental features specific to different parasites.

Reference:
Kaewkamnerd, S., Uthaipibull, C., Intarapanich, A., Pannarut, M., Chaotheing, S. & Tongsima, S. An automatic device for detection and classification of malaria parasite species in thick blood film. BMC Bioinformatics 13 (Suppl.17): S18 (2012).

This article originally appeared on A-IMBN Research.

Posted on 11 April 2013

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