Segmentation of candidate bacillus objects in images of Ziehl-Neelsen-stained sputum smears using deformable models
Includes abstract. === Includes bibliographical references (leaves 83-88). === Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians and to achieve faster diagnosis in order to cope with the rising number of TB cases. Image processing...
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| Format: | Dissertation |
| Language: | English |
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University of Cape Town
2014
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| Online Access: | http://hdl.handle.net/11427/3232 |
| Summary: | Includes abstract. === Includes bibliographical references (leaves 83-88). === Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians and to achieve faster diagnosis in order to cope with the rising number of TB cases. Image processing techniques provide a useful alternative to the conventional, manual analysis of sputum smears for diagnosis. In the project described here, the use of parametric and geometric deformable models was explored for segmentation of TB bacilli in images of Ziehl-Neelsen-stained sputum smears for automated TB diagnosis. The goal of segmentation is to produce candidate bacillus objects for input into a classifier. |
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