Machine Vision and Applications

, Volume 22, Issue 3, pp 461–469

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

Authors

    • Institute of Bioengineering and Nanotechnology
  • S. H. Ong
    • Division of Bioengineering, Department of Electrical and Computer EngineeringNational University of Singapore
  • K. S. W. Tan
    • Department of MicrobiologyNational University of Singapore
Original Paper

DOI: 10.1007/s00138-010-0284-x

Cite this article as:
Kumarasamy, S.K., Ong, S.H. & Tan, K.S.W. Machine Vision and Applications (2011) 22: 461. doi:10.1007/s00138-010-0284-x

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.

Keywords

Edge detectionGestalt lawsMalariaSegmentationSupport vector machine

Copyright information

© Springer-Verlag 2010