Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/13451
ชื่อเรื่อง: Automated detection of plasmodium falciparum from Giemsa-stained thin blood films
ผู้แต่ง: Preedanan W.
Phothisonothai M.
Senavongse W.
Tantisatirapong S.
Keywords: Feature extraction
Image segmentation
Statistical methods
Accuracy of classifications
Adaptive thresholding
Automated detection
Automatic segmentations
Feature extraction and classification
Leave-one-out cross validations
Plasmodium falciparum
Statistical features
Blood
วันที่เผยแพร่: 2016
บทคัดย่อ: 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.
URI: https://ir.swu.ac.th/jspui/handle/123456789/13451
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966658983&doi=10.1109%2fKST.2016.7440501&partnerID=40&md5=c6f91c2517dfef3d70a3f381a7826f2a
Appears in Collections:Scopus 1983-2021

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