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

Abstract

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.

Figure 1. Results of the MoG pixel classifier overlaid on a sub-image of the test dataset

Figure 2. Example outlier objects; (a) clumps of touching bacilli; (b) red stains

Figure 2. Example outlier objects; (a) clumps of touching bacilli; (b) red stains

Figure 3. Results of the MoG pixel classifier on an image with out-of-focus bacilli