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Author: search.filters.author.Phothisonothai, MontriSubject: search.filters.subject.Plasmodium falciparum

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    Classification of in Vitro Blood Stages of Plasmodium Falciparum Based on Fuzzy Inference System
    (2018-08-06)
    Tantisatirapong, Suchada
    ;
    Phothisonothai, Montri
    This paper proposes the automated texture based classification of Malaria parasites in Giemsa-stained thin blood film images based on fuzzy inference system (FIS). The proposed expert and knowledge based framework includes the segmentation, feature extraction and classification of erythrocytes. First-order statistical analysis includes mean, standard deviation, skewness and kurtosis have been proposed as input parameters of FIS. The effectiveness of classifier is compared to find appropriate parame- ters for classification of normal cells and infected cells, both ring and trophozoite stages. The proposed method can provide 96.28% accuracy rate for binary classification of normal and infected cells. The results also yield 97.55% accuracy for ring stage classification, and 98.54% accuracy for trophozoite stage classification.
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    Automated detection of plasmodium falciparum from Giemsa-stained thin blood films
    (2016-03-23)
    Preedanan, Wongsakorn
    ;
    Phothisonothai, Montri
    ;
    Senavongse, Wongwit
    ;
    Tantisatirapong, Suchada
    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.