ArticleIEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society2010
Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears.
Article in IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 119 citations in OpenAlex.
- AI-Based Analysis of Ziehl-Neelsen-Stained Sputum Smears forLife (Basel, Switzerland) · 2024Article
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- DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation.Visual computing for industry, biomedicine, and art · 2023Article
- Identification of the selected soil bacteria genera based on their geometric and dispersion features.PloS one · 2023Article
- A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl-Neelsen Stain on Tissue.Diagnostics (Basel, Switzerland) · 2022Article
- Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Automatic microscopic detection of mycobacteria in sputum: a proof-of-concept.Scientific reports · 2018Article
- A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing.Sensors (Basel, Switzerland) · 2016Article
- A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.Journal of medical systems · 2016Review
- Clinical microbiology informatics.Clinical microbiology reviews · 2014Review
- Article
- Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy.Computer methods and programs in biomedicine · 2012Article
- Automated tuberculosis diagnosis using fluorescence images from a mobile microscope.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2012Article
- Automated focusing in bright-field microscopy for tuberculosis detection.Journal of microscopy · 2010Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.
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