Automated detection of tuberculosis in Ziehl‐Neelsen‐stained sputum smears using two one‐class classifiers

Summary 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.