Automated detection of plasmodium falciparum from Giemsa-stained thin blood films
| Autor: | Montri Phothisonothai, Suchada Tantisatirapong, Wongwit Senavongse, Wongsakorn Preedanan |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
biology Computer science business.industry Feature extraction Pattern recognition Plasmodium falciparum Parasitemia medicine.disease biology.organism_classification Thresholding Giemsa stain Support vector machine 03 medical and health sciences 030104 developmental biology Binary classification parasitic diseases medicine Segmentation Computer vision Artificial intelligence business |
| Zdroj: | KST |
| DOI: | 10.1109/kst.2016.7440501 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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