Classification of mycobacterium tuberculosis in images of ZN-stained sputum smears

Authors:
Rethabile Khutlang;Sriram Krishnan;Ronald Dendere;Andrew Whitelaw;Konstantinos Veropoulos;Genevieve Learmonth;Tania S. Douglas
Affiliations:
Medical Research Council, UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, South Africa;Medical Research Council, UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, South Africa;Medical Research Council, UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, South Africa;Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa;Health-Safety and Environmental Services, General Elecric Healthcare, Medical Systems Hellas, Athens, Greece and Guardian Technologies International, Inc., Herndon, VA;Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa;Medical Research Council, UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, South Africa
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

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Abstract

Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as jndged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95 % for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.