A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.
BACKGROUND
Malaria parasitemia is commonly used as a measurement of the amount of parasites in the patient's blood and a crucial indicator for the degree of infection. Manual evaluation of Giemsa-stained thin blood smears under the microscope is onerous, time consuming and subject to human error. Although automatic assessments can overcome some of these problems the available methods are currently limited by their inability to evaluate cases that deviate from a chosen "standard" model.


RESULTS
In this study reliable parasitemia counts were achieved even for sub-standard smear and image quality. The outcome was assessed through comparisons with manual evaluations of more than 200 sample smears and related to the complexity of cell overlaps. On average an estimation error of less than 1% with respect to the average of manually obtained parasitemia counts was achieved. In particular the results from the proposed approach are generally within one standard deviation of the counts provided by a comparison group of malariologists yielding a correlation of 0.97. Variations occur mainly for blurred out-of-focus imagery exhibiting larger degrees of cell overlaps in clusters of erythrocytes. The assessment was also carried out in terms of precision and recall and combined in the F-measure providing results generally in the range of 92% to 97% for a variety of smears. In this context the observed trade-off relation between precision and recall guaranteed stable results. Finally, relating the F-measure with the degree of cell overlaps, showed that up to 50% total cell overlap can be tolerated if the smear image is well-focused and the smear itself adequately stained.


CONCLUSIONS
The automatic analysis has proven to be comparable with manual evaluations in terms of accuracy. Moreover, the test results have shown that the proposed comparison-based approach, by exploiting the interrelation between different images and color channels, has successfully overcome most of the inherent limitations possibly occurring during the sample preparation and image acquisition phase. Eventually, this can be seen as an opportunity for developing low-cost solutions for mass screening.
Le
Minh-Tam
MT
School of Computer Engineering, Nanyang Technological University, N4-02a-32 Nanyang Avenue, Singapore 639798, Singapore. minhtamle@ntu.edu
Bretschneider
Timo R
TR
Kuss
Claudia
C
Preiser
Peter R
PR
D000818:Animals; D001399:Azure Stains; D002452:Cell Count / Q000379:methods*; D004396:Coloring Agents; D004906:Erythrocyte Count / Q000379:methods; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D016778:Malaria, Falciparum / Q000097:blood* / Q000175:diagnosis* / Q000469:parasitology; D008403:Mass Screening / Q000379:methods; D015588:Observer Variation; D018512:Parasitemia / Q000175:diagnosis*; D010963:Plasmodium falciparum / Q000166:cytology*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013194:Staining and Labeling / Q000379:methods
D001399:Azure Stains; D004396:Coloring Agents