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

作者：Khutlang R; Krishnan S; Whitelaw A; Douglas T S^*

来源：Journal of Microscopy, 2010, 237(1): 96-102.

DOI：10.1111/j.1365-2818.2009.03308.x

• Automated microscopy
• classifiers
• segmentation
• tuberculosis
• Ziehl-Neelsen

P>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.

• 出版日期2010-1