Fractal dimension estimation for texture images: a parallel approach
Pattern Recognition Letters
Distance measure and induced fuzzy entropy
Fuzzy Sets and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Threshold selection using fuzzy set theory
Pattern Recognition Letters
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
Segmentation of malaria parasites in peripheral blood smear images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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This paper aims at introducing a textural pattern analysis approach to Plasmodium vivax P. vivax detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of four stages - artefacts reduction, fuzzy divergence-based segmentation of P. vivax infected regions and normal erythrocytes, textural feature extraction using grey level co-occurrence matrix and fractal dimension, finally classification. Here, we have extracted seven features, out of which five are statistically significant in discriminating textures between malaria and normal classes based on light microscopic blood images at 100× resolutions. Finally, Bayesian and support vector machine-based classifiers are trained and validated with 100 cases and 100 control subjects. In effect, it is hereby observed that the significant textural features lead to discriminate P. vivax with 95% and 98% accuracies for SVM and Bayesian classifiers respectively. Results are studied and compared.