Segmentation and classification of tuberculosis bacilli from ZN-stained sputum smear images

• Published in: 2009 IEEE International Conference on Automation Science and Engineering pp. 217 - 220
• Main Authors: Makkapati, V., Agrawal, R., Acharya, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Automation; Color; Fluorescence; Image analysis; Image segmentation; Microscopy; Multi-layer neural network; Shape; Yarn; Zinc
• ISBN: 1424445787; 9781424445783
• ISSN: 2161-8070
• DOI: 10.1109/COASE.2009.5234173

Quality of tuberculosis (TB) diagnosis by manual observation varies depending on the quality of the smear and skill of the pathologist. To overcome this problem, a method for diagnosis of TB from ZN-stained sputum smear images is presented in this paper. Hue color component based approach is proposed to segment the bacilli by adaptive choice of the hue range. The bacilli are declared to be valid or invalid depending on the presence of beaded structure inside them. The beaded structure is segmented by thresholding the saturation component of the bacilli pixels. Clumps of bacilli and other artifacts are removed by thresholding the area, thread length and thread width parameters of the bacilli. Results presented for several images taken from different patients show that the scheme detects the presence of TB accurately.