Publication: Automated detection of plasmodium falciparum from Giemsa-stained thin blood films
0
0
Issued Date
2016
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-84966658983
Rights Holder(s)
Scopus
Bibliographic Citation
2016 8th International Conference on Knowledge and Smart Technology, KST 2016. (2016), p.215-218
Suggested Citation
Preedanan W., Phothisonothai M., Senavongse W., Tantisatirapong S. Automated detection of plasmodium falciparum from Giemsa-stained thin blood films. 2016 8th International Conference on Knowledge and Smart Technology, KST 2016. (2016), p.215-218. doi:10.1109/KST.2016.7440501 Retrieved from: https://hdl.handle.net/20.500.14740/5540
Abstract
This paper investigates automated detection of malaria parasites in images of Giemsa-stained thin blood films. We aim to determine parasitemia based on automatic segmentation, feature extraction and classification methods. Segmentation relies on adaptive thresholding and watershed methods. Statistical features are then computed for each cell and classified using SVM binary classifier. Accuracy of classification is validated based on the leave-one-out cross-validation technique. This processing pipeline is applied on total 15 images of Giemsa-stained thin blood films and yields 92.71% sensitivity, 97.35% specificity and 97.17% accuracy. © 2016 IEEE.