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dc.contributor.authorClaybon, Swazoo IIIen
dc.date.accessioned2017-06-21T08:01:51Zen
dc.date.available2017-06-21T08:01:51Zen
dc.date.issued2017-06-20en
dc.identifier.othervt_gsexam:11172en
dc.identifier.urihttp://hdl.handle.net/10919/78237en
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
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttuberculosisen
dc.subjectFrangi filteren
dc.subjectimage processingen
dc.subjectHessianen
dc.titleAutomated Fluorescence Microscopy Determination of Mycobacterium Tuberculosis Count via Vessel Filteringen
dc.typeThesisen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.contributor.committeechairWicks, Alfred L.en
dc.contributor.committeememberMuelenaer, Penelopeen
dc.contributor.committeememberBeex, Aloysius A.en


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