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Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

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7 Author(s)
Khutlang, R. ; Dept. of Human Biol., Univ. of Cape Town (UCT), Cape Town, South Africa ; Krishnan, S. ; Dendere, R. ; Whitelaw, A.
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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 judged 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.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:14 ,  Issue: 4 )