Publication: An automatic vision-based malaria diagnosis system
Issued Date
2013-06-01
Resource Type
ISSN
13652818
00222720
00222720
Other identifier(s)
2-s2.0-84877670525
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Microscopy. Vol.250, No.3 (2013), 166-178
Suggested Citation
J. P. Vink, M. Laubscher, R. Vlutters, K. Silamut, R. J. Maude, M. U. Hasan, G. De Haan An automatic vision-based malaria diagnosis system. Journal of Microscopy. Vol.250, No.3 (2013), 166-178. doi:10.1111/jmi.12032 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/32316
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
An automatic vision-based malaria diagnosis system
Other Contributor(s)
Abstract
Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.