Detection of tuberculosis bacilli from microscopic sputum smear images

Tuberculosis is a contagious illness caused by the Mycobacterium Tuberculosis, also known as Koch bacillus. Many developing countries follow the manual method for diagnosing TB, which causes false alarms in the detection of TB positive and negative. In order to reduce the intervention of human we have developed an effective algorithm as an automated system for the detection of tuberculosis bacilli. This paper proposes a color segmentation and classification approach for automatic detection of Mycobacterium Tuberculosis, which causes TB from the image of Ziehl-Nielsen stained sputum smear obtained from a bright microscope. Segment the bacilli called candidate bacilli using its characteristics from the image using Particle Swarm Optimization technique, depending on pixel intensities. The candidate bacilli are then grouped together using connected component analysis after using morphological operations. Detection of Tuberculosis bacilli from sputum smear by random forest technique is a prominent method used in diagnosing the tuberculosis by classifying the subject samples. The combination of particle swarm optimization and random forest classification provides better results and correct diagnosis in term of infection level. The experimental result shows that our approach is significantly better compared to the existing approaches.