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

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dc.contributor.advisorDouglas, Tania Sen_ZA
dc.contributor.authorPatel, Bhavinen_ZA
dc.date.accessioned2014-07-28T18:14:12Z
dc.date.available2014-07-28T18:14:12Z
dc.date.issued2010en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references (leaves 140-144).
dc.description.abstractAutomated 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.identifier.apacitationPatel, 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/11427/3191en_ZA
dc.identifier.chicagocitationPatel, 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/11427/3191en_ZA
dc.identifier.citationPatel, 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.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
dc.identifier.urihttp://hdl.handle.net/11427/3191
dc.identifier.vancouvercitationPatel 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/11427/3191en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Human Biologyen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMedicineen_ZA
dc.titleCreating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopyen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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