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 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. Background Tuberculosis (TB) is caused by Mycobacterium tuberculosis, which enters the body through the lungs and which, expectorated in sputum, can be seen as clusters or individually in stained sputum smears under a microscope. Two staining methods are used: auramine, which requires fluorescence microscopy; and Ziehl-Neelsen (ZN), which requires bright-field microscopy