Tuberculosis Disease Detection Using Image Processing

Authors(1) :-Rajkumar. M

Tuberculosis is one of the deadliest disease in this world especially in developing countries. Sputum smear microscopy is the main diagnostic tool in developing countries and high burden countries for the detection of tuberculosis. Previous studies shows that the manual screening for sputum smear microscopic images lead to misdiagnosis and false result. Image processing techniques are applied in this research to enhance, segment and classify the sputum smear images for computerized process of tuberculosis bacilli identification. Image processing algorithm which used for sputum smear image include a series of enhancement techniques, segmentation methods and morphological operation. As the non-bacillus objects in sputum smear image can bias the detection, it should be suppressed from the smear image. This paper employs color image segmentation technique for segmenting the tuberculosis bacillus objects from the background. Tuberculosis bacillus objects are segmented from the background in two stages based on color space conversion and k-means clustering to identify the tuberculosis bacilli. The proposed method uses thirteen texture feature extraction and makes the judgement using multi-support vector machine. The experiment result confirmed the superior performance of the proposed method.

Authors and Affiliations

Rajkumar. M
Department of Computer Science, Pondicherry University, Puducherry, India

Image Acquisition, Preprocessing, Segmentation, Feature Extraction, Classification

  1. VanDeun A, Salim AH, Cooreman E, Hossain MA, RemaA,etal,"Optimal Tuberculosis case detection by direct sputum smear microscopy: how much better is more?".Int J Tuber Lung Dis 6, (2002) , P 222-230 World Health Organization, Global Tuberculosis Report 2014.
  2. UNITAID, Tuberculosis: diagnostic, technology and market landscape. 2014, WHO: Geneva. p. 1–42.
  3. Sputum Gram stain-Overview, University of Maryland Medical Center www.umm.edu/ency/article/
  4. www.tbfacts.org/tb-tests
  5. Biomedical image analysis book.
  6. RonaldDendere, segmentation of candidate bacillus objects in images of ziehl-neelsen-stained sputum smears using deformable models, University of Cape Town February 2009.
  7. https://en.wikipedia.org/wiki/Lab_color_space accessed on 3/5/2016
  8. R.Gonzalez and R. Woods Digital Image Processing, Addison-Wesley Publishing Company, 1992.
  9. Dong ping Tian, A Review on Image Feature Extraction and Representation Techniques, International Journal of Multimedia and Ubiquitous Engineering Vol. 8, No. 4, July,2013.
  10. M. Yang, K. Kpalma and J. Ronsin. "A survey of shape feature extraction techniques", Pattern Recognition, (2008), pp. 43-90.
  11. Sawantet al., International Journal of Advanced Research in Computer Science and Software Engineering.
  12. www.healthdiscoverycorp.com/svm.php
  13. Marzieh Ghiasi &Tripti Pande1 & MadhukarPai , Advances in Tuberculosis Diagnostics.
  14. Costa, M. G.F., Costa Filho, C. F. F., Sena, J. F., Salen, J. & Lima, M. O., Automatic identification of mycobacterium tuberculosis with conventional light microscopy, Proceedings of the 30th Annual International Conference of the IEEE EMBS,(2008), pp. 382- 385,Vancouver, British Columbia, Canada.
  15. Sadaphal, P, Rao, J., Comstock, G.W. & Beg, M.F., Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains. International Journal of Tuberculosis Lung Disease, (2008),Vol. 12, n. 5, pp. 579-582, ISSN 1027-3719.
  16. Khutlang, R., Krishnan, S., Dendere, R., Whitelaw, A., Veropoulos, K., Learmonth, G. Douglas, T. S. (2010) , Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears, IEEE Trans InfTechnol Biomed. 2010 July; 14(4): 949–957. doi:10.1109/TITB.2009.2028339.
  17. M. K. Osman , M. Y. Mashor, H. Jaafar , Detection of Tuberculosis Bacilli in Tissue Slide Images using HMLP Network Trained by Extreme Learning Machine ,electronics and electrical engineering , 2012. No. 4(120) , ISSN 1392-1215, T 125 .
  18. Shan-e-Ahmed Raza, M. QaisarMarjan, Muhammad Arif, Farhana Butt , Faisal Sultan, Nasir M. Rajpoot , Anisotropic Tubular Filtering for Automatic Detection of Acid-Fast Bacilli in Digitized Microscopic Images of Ziehl-Neelsen Stained Sputum Smear Samples.
  19. Kusworo Adi1, RahmadGernowo, ArisSugiharto , K. Sofjan F , Adi P , Ari B ,tuberculosis (TB) identification in the zihel-nelseen sputum sample in NTSC channel and support vector machine (SVM) classification , International Journal of Innovative Research in Science, Engineering and Technology ,Vol. 2, Issue 9, September 2013 , ISSN: 2319-8753.
  20. Rachna H.B., M.S.MallikarjunaSwamy ,detection of tuberculosis bacilli using image processing techniques ,international journal of soft computing and engineering .issn:2231-2307,volume-3,issue-4 ,September 2013.
  21. JadhavMukti , Kale K.V , analysis of ZN-stained sputum smear enhanced images for identification of mycobacterium tuberculosis bacilli cells ,international journal of computer applications,vol.23 , no.5 , June 2011.
  22. Ibnu Siena, Kusworo Adi, Rahmat Gernowoand Nelly Mirnasari, Development of Algorithm Tuberculosis Bacteria Identification Using Color Segmentation and Neural Networks, International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol:12 No:04.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1012-1020
Manuscript Number : CSEIT1833344
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Rajkumar. M, "Tuberculosis Disease Detection Using Image Processing", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1012-1020, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833344

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