Detection of tuberculosis in sputum smear images using two one-class classifiers

Authors:
Rethabile Khutlang;Sriram Krishnan;Andrew Whitelaw;Tania S. Douglas
Affiliations:
Medical Imaging Research Unit, Department of Human Biology, University of Cape Town;Medical Imaging Research Unit, Department of Human Biology, University of Cape Town;Division of Medical Microbiology, University of Cape Town;Medical Imaging Research Unit, Department of Human Biology, University of Cape Town
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

We present a method for the identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen stained sputum smears obtained using a bright field microscope. We use two stages of classification; the first is a one-class pixel classifier, after which geometric transformation invariant features are extracted. The second stage is a one-class object classifier. Different classifiers are compared; the sensitivity of all tested classifiers is above 90% for the identification of a single bacillus object using all extracted features. Our results may be used to reduce technician involvement in screening for tuberculosis, and will be particularly useful in laboratories in countries with a high burden of tuberculosis.