Publication: Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films
0
0
Issued Date
2020
Resource Type
File Type
application/pdf
ISSN
16859545
Other identifier(s)
2-s2.0-85083699782
Rights Holder(s)
Scopus
Bibliographic Citation
ECTI Transactions on Electrical Engineering, Electronics, and Communications. Vol 18, No.1 (2020), p.9-16
Suggested Citation
Tantisatirapong S., Preedanan W. Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films. ECTI Transactions on Electrical Engineering, Electronics, and Communications. Vol 18, No.1 (2020), p.9-16. doi:10.37936/ecti-eec.2020181.208115 Retrieved from: https://hdl.handle.net/20.500.14740/4649
Author(s)
Abstract
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).