Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00138-010-0284-x
Title: Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection
Authors: Kumarasamy, S.K.
Ong, S.H. 
Tan, K.S.W.
Keywords: Edge detection
Gestalt laws
Malaria
Segmentation
Support vector machine
Issue Date: May-2011
Source: Kumarasamy, S.K., Ong, S.H., Tan, K.S.W. (2011-05). Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection. Machine Vision and Applications 22 (3) : 461-469. ScholarBank@NUS Repository. https://doi.org/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. © Springer-Verlag 2010.
Source Title: Machine Vision and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/57296
ISSN: 09328092
DOI: 10.1007/s00138-010-0284-x
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