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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Khutlang, R. Krishnan, S. Whitelaw, A. Douglas, T.S. |
| Copyright Year | 2009 |
| Description | Author affiliation: Division of Medical Microbiology, University of Cape Town, South Africa (Whitelaw, A.) || Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, South Africa (Khutlang, R.; Krishnan, S.; Douglas, T.S.) |
| Abstract | We present a method for the 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 is a one-class pixel classifier, after which geometric transformation invariant features are extracted. The second stage is a one-class object classifier. 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. Our results may be used to reduce technician involvement in screening for tuberculosis, and will be particularly useful in laboratories in countries with a high burden of tuberculosis. |
| Starting Page | 1007 |
| Ending Page | 1010 |
| File Size | 515976 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193225 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Microscopy Image segmentation Pixel Image edge detection Feature extraction Biomedical imaging Cities and towns Object detection Bayesian methods Africa one-class classification Tuberculosis Zeihl-Neelsen microscopy pixel classifiers feature extraction |
| Content Type | Text |
| Resource Type | Article |
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