Automated detection of tuberculosis in Ziehl-Neelsen-stained sputum smears using two one-class classifiers.
Screening for tuberculosis in high-prevalence countries relies on sputum smear microscopy. We present a method for the automated 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 comprises a one-class pixel classifier for object segmentation. Geometric transformation invariant features are extracted for implementation of the second stage, namely one-class object classification. 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. The mixture of Gaussians classifier performed well in both stages of classification. This method may be used as a step in the automation of tuberculosis screening, in order to reduce technician involvement in the process.
Khutlang
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R
MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town.
Krishnan
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S
Whitelaw
A
A
Douglas
T S
TS
D000465:Algorithms; D057205:Automation, Laboratory; D003116:Color; D006801:Humans; D008403:Mass Screening; D009169:Mycobacterium tuberculosis / Q000166:cytology / Q000302:isolation & purification; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity; D013183:Sputum / Q000382:microbiology*; D013194:Staining and Labeling; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000382:microbiology