  Tuberculosis is a contagious disease which is one of the leading causes of
death, globally. The general diagnosis methods for tuberculosis include
microscopic examination, tuberculin skin test, culture method, enzyme linked
immunosorbent assay (ELISA) and electronic nose system. World Health
Organization (WHO) recommends standard microscopic examination for early
diagnosis of tuberculosis. In microscopy, the technician examines field of
views (FOVs) in sputum smear for presence of any TB bacilli and counts the
number of TB bacilli per FOV to report the level of severity. This process is
time consuming with an increased concentration for an experienced staff to
examine a single sputum smear. The examination demands for skilled technicians
in high-prevalence countries which may lead to overload, fatigue and diminishes
the quality of microscopy. Thus, a computer assisted system is proposed and
designed for the detection of tuberculosis bacilli to assist pathologists with
increased sensitivity and specificity. The manual efforts in detecting and
counting the number of TB bacilli is greatly minimized. The system obtains
Ziehl-Neelsen stained microscopic images from conventional microscope at 100x
magnification and passes the data to the detection system. Initially the
segmentation of TB bacilli was done using RGB thresholding and Sauvola's
adaptive thresholding algorithm. To eliminate the non-TB bacilli from coarse
level segmentation, shape descriptors like area, perimeter, convex hull, major
axis length and eccentricity are used to extract only the TB bacilli features.
Finally, the TB bacilli are counted using the generated bounding boxes to
report the level of severity.
