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dc.contributor.authorClaybon III, Swazooen_US
dc.date.accessioned2017-06-21T08:01:51Z
dc.date.available2017-06-21T08:01:51Z
dc.date.issued2017-06-20en_US
dc.identifier.othervt_gsexam:11172en_US
dc.identifier.urihttp://hdl.handle.net/10919/78237
dc.description.abstractTuberculosis (TB), a deadly infectious disease caused by the bacillus Mycobacterium tuberculosis (MTB), is the leading infectious disease killer globally, ranking in the top 10 overall causes of death despite being curable with a timely diagnosis and the correct treatment [3]. As such, eradicating tuberculosis (TB) is one of the targets of the Sustainable Development Goals (SDGs) for global health as approved by the World Health Assembly (WHA) in 2014 [2,3]. This work describes an automated method of screening and determining the severity, or count, of the TB infection in patients via images of fluorescent TB on a sputum smear. Using images from a previously published dataset [9], the algorithm involves a vessel filter which uses the second derivative information in an image by looking at the eigenvalues of the Hessian matrix. Finally, filtering for size and by using background subtraction techniques, each bacillus is effectively isolated in the image. The primary objective was to develop an image processing algorithm in Python that can accurately detect Mycobacteria bacilli in an image for a later deployment in an automated microscope that can improve the timeliness of accurate screenings for acid-fast bacilli (AFB) in a high-volume healthcare setting. Major findings include comparable average and overall object level precision, recall, and F1-score results as compared to the support vector machine (SVM) based algorithm from Chang et al. [9]. Furthermore, this work's algorithm is more accurate on the field level infectiousness accuracy, based on F1-score results, and has a high visual semantic accuracy.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjecttuberculosisen_US
dc.subjectFrangi filteren_US
dc.subjectimage processingen_US
dc.subjectHessianen_US
dc.titleAutomated Fluorescence Microscopy Determination of Mycobacterium Tuberculosis Count via Vessel Filteringen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairWicks, Alfred L.en_US
dc.contributor.committeememberMuelenaer, Penelope Benavitzen_US
dc.contributor.committeememberBeex, Aloysius A.en_US


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