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. 2010 Jan;237(1):96-102.
doi: 10.1111/j.1365-2818.2009.03308.x.

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

R Khutlang  1 , S Krishnan, A Whitelaw, T S Douglas
Affiliations

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

R Khutlang et al. J Microsc. 2010 Jan.

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.

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Figures

Figure 1
Figure 1. Results of the MoG pixel classifier overlaid on a sub-image of the test dataset
Figure 2
Figure 2. Example outlier objects; (a) clumps of touching bacilli; (b) red stains
Figure 2
Figure 2. Example outlier objects; (a) clumps of touching bacilli; (b) red stains
Figure 3
Figure 3. Results of the MoG pixel classifier on an image with out-of-focus bacilli
See this image and copyright information in PMC

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