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Tuberculosis is a contagious disease which is one of the leading causes ofdeath, globally. The general diagnosis methods for tuberculosis includemicroscopic examination, tuberculin skin test, culture method, enzyme linkedimmunosorbent assay (ELISA) and electronic nose system. World HealthOrganization (WHO) recommends standard microscopic examination for earlydiagnosis of tuberculosis. In microscopy, the technician examines field ofviews (FOVs) in sputum smear for presence of any TB bacilli and counts thenumber of TB bacilli per FOV to report the level of severity. This process istime consuming with an increased concentration for an experienced staff toexamine a single sputum smear. The examination demands for skilled techniciansin high-prevalence countries which may lead to overload, fatigue and diminishesthe quality of microscopy. Thus, a computer assisted system is proposed anddesigned for the detection of tuberculosis bacilli to assist pathologists withincreased sensitivity and specificity. The manual efforts in detecting andcounting the number of TB bacilli is greatly minimized. The system obtainsZiehl-Neelsen stained microscopic images from conventional microscope at 100xmagnification and passes the data to the detection system. Initially thesegmentation of TB bacilli was done using RGB thresholding and Sauvola'sadaptive thresholding algorithm. To eliminate the non-TB bacilli from coarselevel segmentation, shape descriptors like area, perimeter, convex hull, majoraxis length and eccentricity are used to extract only the TB bacilli features.Finally, the TB bacilli are counted using the generated bounding boxes toreport the level of severity.

Design to automate the detection and counting of Tuberculosis(TB) bacilli