The Automated Identification of Tubercle Bacilli using Image Processing and Neural Computing Techniques

Tuberculosis is currently the world's leading cause of adult death from a single infectious disease. Sputum examination remains the cornerstone of diagnosis in epidemic situations. To improve the diagnostic process we are developing an automated method for the detection of tubercle bacilli in clinical specimens, principally sputum smears. A preliminary investigation is presented here, which makes use of image processing techniques and neural network classifiers for the automatic identification of TB bacilli on Auramine stained sputum specimens. Currently, the developed system shows a sensitivity of 93.5% for the identification of individual bacilli. As there are usually fairly numerous TB bacilli in the sputum of patients with active pulmonary TB, the overall diagnostic accuracy for sputum smear positive patients is expected to be very high. Potential benefits of automated screening for TB are rapid and accurate diagnosis, increased screening of the population, and reduced health risk to staff processing slides.