Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy

 

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dc.contributor.advisor Douglas, Tania S en_ZA
dc.contributor.author Patel, Bhavin en_ZA
dc.date.accessioned 2014-07-28T18:14:12Z
dc.date.available 2014-07-28T18:14:12Z
dc.date.issued 2010 en_ZA
dc.identifier.citation Patel, B. 2010. Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/3191
dc.description Includes abstract.
dc.description Includes bibliographical references (leaves 140-144).
dc.description.abstract Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians in busy TB laboratories and to achieve faster diagnosis in countries with a heavy TB burden. As a step in the development of an automated microscope, the project described here was concerned with microscope auto-positioning; this primarily involves generating a point of reference on a slide, which can be used to automatically bring desired fields on the slide to the field-of-view of the microscope for re-examination. The study was carried out using a conventional microscope and Ziehl- Neelsen (ZN) stained sputum smear slides. All images were captured at 40x magnification. A digital replication, the virtual slide map, of an actual slide was constructed by combining the manually acquired images of the different fields of the slide. The geometric hashing scheme was found to be suitable for auto-stitching a large number of images (over 300 images) to form a virtual slide map. An object recognition algorithm, which was also based on the geometric hashing technique, was used to localise a query image (the current field-of-view) on the virtual slide map. This localised field-of-view then served as the point of reference. The true positive (correct localisation of a query image on the virtual slide map) rate achieved by the algorithm was above 88% even for noisy query images captured at slide orientations up to 26°. The image registration error, computed as the average mean square error, was less than 14 pixel2 (corresponding to 1.02 μm2 and 0.001% error in an image measuring 1030 x 1300 pixels) corresponding to a root mean square registration error of 3.7 pixels. Superior image registration accuracy was obtained at the expense of time using the scale invariant feature transform (SIFT), with a image registration error of 1 pixel2 (0.07 μm2). The object recognition algorithm is inherently robust to changes in slide orientation and placement, which are likely to occur in practice as it is impossible to place the slide in exactly the same position on the microscope at different times. Moreover, the algorithm showed high tolerance to illumination changes and robustness to noise. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Medicine en_ZA
dc.title Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Department of Human Biology en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Patel, B. (2010). <i>Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Department of Human Biology. Retrieved from http://hdl.handle.net/11427/3191 en_ZA
dc.identifier.chicagocitation Patel, Bhavin. <i>"Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Department of Human Biology, 2010. http://hdl.handle.net/11427/3191 en_ZA
dc.identifier.vancouvercitation Patel B. Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Department of Human Biology, 2010 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/3191 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Patel, Bhavin AB - Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians in busy TB laboratories and to achieve faster diagnosis in countries with a heavy TB burden. As a step in the development of an automated microscope, the project described here was concerned with microscope auto-positioning; this primarily involves generating a point of reference on a slide, which can be used to automatically bring desired fields on the slide to the field-of-view of the microscope for re-examination. The study was carried out using a conventional microscope and Ziehl- Neelsen (ZN) stained sputum smear slides. All images were captured at 40x magnification. A digital replication, the virtual slide map, of an actual slide was constructed by combining the manually acquired images of the different fields of the slide. The geometric hashing scheme was found to be suitable for auto-stitching a large number of images (over 300 images) to form a virtual slide map. An object recognition algorithm, which was also based on the geometric hashing technique, was used to localise a query image (the current field-of-view) on the virtual slide map. This localised field-of-view then served as the point of reference. The true positive (correct localisation of a query image on the virtual slide map) rate achieved by the algorithm was above 88% even for noisy query images captured at slide orientations up to 26°. The image registration error, computed as the average mean square error, was less than 14 pixel2 (corresponding to 1.02 μm2 and 0.001% error in an image measuring 1030 x 1300 pixels) corresponding to a root mean square registration error of 3.7 pixels. Superior image registration accuracy was obtained at the expense of time using the scale invariant feature transform (SIFT), with a image registration error of 1 pixel2 (0.07 μm2). The object recognition algorithm is inherently robust to changes in slide orientation and placement, which are likely to occur in practice as it is impossible to place the slide in exactly the same position on the microscope at different times. Moreover, the algorithm showed high tolerance to illumination changes and robustness to noise. DA - 2010 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2010 T1 - Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy TI - Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy UR - http://hdl.handle.net/11427/3191 ER - en_ZA


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