Correlation between automatic detection of malaria on thin film and experts' parasitaemia scores

An algorithm was developed to diagnose the presence of malaria and to estimate the depth of infection by automatically counting individual normal and infected erythrocytes in images of thin blood smears. During the training stage, the parameters of the algorithm were optimized to maximize correlation with estimates of parasitaemia from expert human observers. The correlation was tested on a set of 1590 images from seven thin film blood smears. The correlation between the results from the algorithm and expert human readers was r = 0.836. Results indicate that reliable estimates of parasitaemia may be achieved by computational image analysis methods applied to images of thin film smears. Meanwhile, compared to biological experiments, the algorithm fitted well the three high parasitaemia slides and a mid-level parasitaemia slide, and overestimated the three low parasitaemia slides. To improve the parasitaemia estimation, the sources of the overestimation were identified. Emphasis is laid on the importance of further research in order to identify parasites independently of their erythrocyte hosts.