Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears.
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.
Khutlang
Rethabile
R
Medical Research Council/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town 7701, South Africa.
Krishnan
Sriram
S
Dendere
Ronald
R
Whitelaw
Andrew
A
Veropoulos
Konstantinos
K
Learmonth
Genevieve
G
Douglas
Tania S
TS
D000465:Algorithms; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000145:classification*; D012680:Sensitivity and Specificity; D013183:Sputum / Q000382:microbiology*; D013194:Staining and Labeling