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

• URL: http://arxiv.org/abs/2105.11432v1
• Date: Mon, 24 May 2021 17:41:39 GMT
• Title: Design to automate the detection and counting of Tuberculosis(TB) bacilli
• Authors: Dinesh Jackson Samuel and Rajesh Kanna Baskaran
• Abstract summary: 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. A computer assisted system is proposed and designed for the detection of tuberculosis bacilli to assist pathologists with increased sensitivity and specificity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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