Detection of Tuberculosis Bacilli using Image Processing Techniques
Rachna H. B.1, M. S. Mallikarjuna Swamy2
1Rachna H.B., Instrumentation Technology, S.J. College of Engineering, Mysore, India.
2M. S. Mallikarjuna Swamy,, Instrumentation Technology, S. J. College of Engineering, Mysore, India.
Manuscript received on August 04, 2013. | Revised Manuscript received on August 28, 2013. | Manuscript published on September 05, 2013. | PP: 47-52 | Volume-3, Issue-4, September 2013. | Retrieval Number: D1768093413/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tuberculosis (TB) is one of the major diseases in developing countries. TB detection is based on sputum examination microscopically by using Ziehl-Neelsen stain (ZN-stain) method, which is used worldwide. This method needs human expertise and intensive examination. The availability of expertise, time and cost are the constraints of the human intervention based examinations. Therefore, there is a need of automation of examination and detection of TB bacteria using digital image of ZN-stain sample. In this work, an algorithm based on image processing is developed for identification of TB bacteria in sputum. The method is based on Otsu thresholding and k-means clustering approach. The performance of clustering and thresholding algorithms for segmenting TB bacilli in tissue sections is compared. The developed automated technique shows good accuracy and efficiency.
Keywords: Clustering, Image segmentation, Otsu thresholding, Tuberculosis.