Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/11997
ชื่อเรื่อง: Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films
ผู้แต่ง: Tantisatirapong S.
Preedanan W.
วันที่เผยแพร่: 2020
บทคัดย่อ: Quantification of parasitaemia is an important part of a microscopic malaria diagnosis. Giemsa-stained thin blood smear is the gold standard method for detecting malaria parasite enumeration. However, manual counting reveals the limitations of human in-consistency and fatigue, as well as the unreliability of accuracy and non-reproducibility. In this paper, the texture-based classification approach is investi-gated. The methods consist of the following pro-cesses: pre-processing, segmentation, feature extrac-tion and the classification of erythrocytes. The pre-processing is applied for image conversion and en-hancement. The segmentation combines local adaptive thresholding, morphological process and water-shed transform to extract red blood cells, separate touching and overlapping cells. Texture analysis is performed to establish parameters obtained from first-order, second-order and higher-order statistical analysis and wavelet transform. Two feature selection approaches, the sequential forward selection method and sequential backward selection method, integrated with a support vector machine classifier are examined to obtain the optimal feature set for identifying the Plasmodium falciparum stages. We found that gray-level co-occurrence matrices based textural features were highly selected. The proposed method produces 98.87% accuracy for binary classification, 99.56% accuracy for ring stage classification, and 99.48% accuracy for tropozoite stage classification. © 2020 Author(s).
URI: https://ir.swu.ac.th/jspui/handle/123456789/11997
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083699782&doi=10.37936%2fecti-eec.2020181.208115&partnerID=40&md5=78d5a06cac1f7a2f77ccfe2294035f10
ISSN: 16859545
Appears in Collections:Scopus 1983-2021

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