Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 42, Issue 2, Pages 296-307Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2008.11.005
Keywords
Malaria parasites; Automatic quantification; Life stage classification; Machine learning; Plasmodium falciparum; Feature extraction; Clump splitting; Pixel color classification; Connected operators; Inclusion-Tree; Cell classification; Classifier evaluation; Multi-class classifiers; Class imbalance
Funding
- Colombian Institute for the advancement of Science (Colciencias) [109-2005]
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Visual quantification of parasitemia in thin blood films is a very tedious, Subjective and time-consuming task. This study presents all original method for quantification and classification of erythrocytes ill stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by in Inclusion-Tree representation that structures the pixel information into objects. from which erythrocytes are found. Finally, a two step classification process identities infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and Average specificity of 91.2%. (C) 2008 Elsevier Inc. All rights reserved.
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