Automated detection of tuberculosis in Ziehl-Neelsen stained sputum smears using two one-class classifiers - Khutlang, Rethabile (2010)
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 implements a one-class pixel classifier, after which geometric transformation invariant features are extracted. The second stage employs 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.