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Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection

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Abstract

We present a novel method for detecting malaria parasites and determining the stage of infection from digital images comprising red blood cells (RBCs). The proposed method is robust under varying conditions of image luminance, contrast and clumping of RBCs. Both strong and weak boundary edges of the RBCs and parasites are detected based on the similarity measure between local image neighborhoods and predefined edge filters. A rule-based algorithm is applied to link edge fragments to form closed contours of the RBCs and parasite regions, as well as to split clumps into constituent cells. A radial basis support vector machine determines the stage of infection from features extracted from each parasite region. The proposed method achieves 97% accuracy in cell segmentation and 86% accuracy in parasite detection when tested on a total of 530 digitally captured images of three species of malaria parasites: Plasmodium falciparum, Plasmodium yoelii and Plasmodium berghei.

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Authors and Affiliations

  1. Institute of Bioengineering and Nanotechnology, 31 Biopolis Way, Singapore, 138669, Singapore

    Saravana Kumar Kumarasamy

  2. Division of Bioengineering, Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore, 119260, Singapore

    S. H. Ong

  3. Department of Microbiology, National University of Singapore, 10 Kent Ridge Crescent, Singapore, 119260, Singapore

    K. S. W. Tan

Authors
  1. Saravana Kumar Kumarasamy
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  2. S. H. Ong
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  3. K. S. W. Tan
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Correspondence to Saravana Kumar Kumarasamy.

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Kumarasamy, S.K., Ong, S.H. & Tan, K.S.W. Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection. Machine Vision and Applications 22, 461–469 (2011). https://doi.org/10.1007/s00138-010-0284-x

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  • DOI: https://doi.org/10.1007/s00138-010-0284-x

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