Tuberculosis (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. / Master of Science / Tuberculosis (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 correct treatment [3]. Furthermore, 3.2 billion are part of an at risk population for contracting tuberculosis, yet 90 % of TB related deaths occur in countries across Africa and other Low and Middle Income Countries (LMICs) [2]. This occurrence is, at least in part, due to a lack of the skilled human resources in LMIC laboratories necessary to scan large numbers of patient specimens and properly screen for TB. Sputum smear microscopy of acid-fast bacilli (AFB) is essential in the screening of TB in high-prevalence countries. With the high rates of TB found in LMICs, there is a need to develop affordable, time-efficient alternatives for lab technicians to effectively screen large volumes of patients. This work describes the development of 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 primary objective of this study was to write a program that can accurately detect tuberculosis in an image for a later deployment in an automated microscope that can improve the timeliness of accurate screening for AFB in a high-volume healthcare setting. Major findings include improved accuracy compared to that of Chang et al.’s machine learning algorithm that was used on this dataset [9].