Automatic Detection of Mycobacterium tuberculosis in Stained Sputum and Urine Smear Images

In this work we present an automatic algorithm for detection of mycobacterium tuberculosis from stained sputum smear images applying a novel method of tubeness filtering in conjunction with Otsu thresholding and connected component analysis. This eliminates human effort and measurement error from the detection of the bacteria, which normally requires a lab technician or pathologist to load a glass slide with a patient’s stained sputum smear into a digital fluorescent microscope and visually search for mycobacterium tuberculosis in several different fields of view. On the other hand, the proposed automated detection of mycobacterium tuberculosis from stained sputum smear digital images renders fast analysis of patient’s sputum for quick and efficient diagnosis of tuberculosis.