Automatic Identification of Mycobacterium tuberculosis in Ziehl-Neelsen Stained Sputum Smear Microscopy Images using a Two-stage Classifier

Lucas de Assis Soares, Klaus Fabian Coco, Evandro Ottoni Teatini Salles, Saulo Bortolon

2015

Abstract

This paper presents a method for the automatic identification of Mycobacterium tuberculosis in Ziehl-Neelsen stained sputum smear microscopy images, the most common bacilloscopy method in developing countries due to its low costs. The proposed method is divided in two stages: a projection of the original coloured image followed by the segmentation and the elimination of large and small segmented structures, and the classification of structures using neural networks and support vector machines. The segmentation of structures presents a loss of bacilli of 1.31 %, while the elimination of areas increases the loss to 14.39 %. The evaluation of the classification of structures is made using cross validation and a maximum sensitivity of 94.25 % is obtained. The presented method has a low computational cost, allying performance and efficiency.

References

  1. Bishop, C. M. et al. (2006). Pattern recognition and machine learning, volume 1. springer New York.
  2. CETELI (2014). An image database of conventional sputum smear microscopy for tuberculosis. Center for Research and Development in Electronic and Information Technology. http://http://www.tbimages.ufam.edu.br/.
  3. Chayadevi, M. and Raju, G. (2014). Automated colour segmentation of tuberculosis bacteria thru region growing: A novel approach. In Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the, pages 154-159. IEEE.
  4. Costa, L. F. and Cesar Jr, R. M. (2000). Shape analysis and classification: theory and practice. CRC press.
  5. Costa, M. G., Costa Filho, C. F., Sena, J. F., Salem, J., and de Lima, M. O. (2008). Automatic identification of mycobacterium tuberculosis with conventional light microscopy. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 382-385. IEEE.
  6. Costa Filho, C. F. F., Levy, P. C., Xavier, C. M., Costa, M. G., Fujimoto, L. B., and Salem, J. (2012). Mycobacterium tuberculosis recognition with conventional microscopy. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pages 6263-6268. IEEE.
  7. Desikan, P. (2013). Sputum smear microscopy in tuberculosis: Is it still relevant? volume 137, pages 442-444.
  8. Duda, R. O., Hart, P. E., and Stork, D. G. (2001). Pattern Classification. Wiley, 2nd edition.
  9. Gonzalez, R. C. and Woods, R. E. (2007). Digital image processing. Prentice Hall, 3rd edition.
  10. Haykin, S. S., Haykin, S. S., Haykin, S. S., and Haykin, S. S. (2009). Neural networks and learning machines, volume 3. Pearson Education Upper Saddle River.
  11. Kusworo, A., Rahmat, G., Aris, S., Adi, P., Ari, B., and Nelly, M. (2013). Autothresholding segmentation for tuberculosis bacteria identification in the ziehlneelsen sputum sample. In Proceedings The 7th International Conference on Information & Communication Technology and Systems (ICTS), pages 15-16.
  12. Makkapati, V., Agrawal, R., and Acharya, R. (2009). Segmentation and classification of tuberculosis bacilli from zn-stained sputum smear images. In Automation Science and Engineering, 2009. CASE 2009. IEEE International Conference on, pages 217-220. IEEE.
  13. Nayak, R., Shenoy, V. P., and Galigekere, R. R. (2010). A new algorithm for automatic assessment of the degree of tb-infection using images of zn-stained sputum smear. In Systems in Medicine and Biology (ICSMB), 2010 International Conference on, pages 294- 299. IEEE.
  14. Sadaphal, P., Rao, J., Comstock, G., and Beg, M. (2008). Image processing techniques for identifying mycobacterium tuberculosis in ziehl-neelsen stains [short communication]. The International Journal of Tuberculosis and Lung Disease, 12(5):579-582.
  15. Siena, I., Adi, K., Gernowo, R., and Miransari, N. (2012). Development of algorithm tuberculosis bacteria identification using color segmentation and neural networks. International Journal of Video and Image Processing and Network Security, 12(4):9-13.
  16. Smart, T. (2007). Background on smear microscopy in tb diagnosis. http://www.aidsmap.com/Background-onsmear-microscopy-in-TB-diagnosis/page/1426650/.
  17. Sotaquirá, M., Rueda, L., and Narvaez, R. (2009). Detection and quantification of bacilli and clusters present in sputum smear samples: a novel algorithm for pulmonary tuberculosis diagnosis. In Digital Image Processing, 2009 International Conference on, pages 117-121. IEEE.
  18. WHO (2014). Tuberculosis (tb). World Health Organization. http://www.who.int/tb/.
  19. Witten, I. H. and Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  20. Zhai, Y., Liu, Y., Zhou, D., and Liu, S. (2010). Automatic identification of mycobacterium tuberculosis from znstained sputum smear: Algorithm and system design. In Robotics and Biomimetics (ROBIO), 2010 IEEE International Conference on, pages 41-46. IEEE.
Download


Paper Citation


in Harvard Style

Soares L., Coco K., Salles E. and Bortolon S. (2015). Automatic Identification of Mycobacterium tuberculosis in Ziehl-Neelsen Stained Sputum Smear Microscopy Images using a Two-stage Classifier . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 186-191. DOI: 10.5220/0005345201860191


in Bibtex Style

@conference{visapp15,
author={Lucas de Assis Soares and Klaus Fabian Coco and Evandro Ottoni Teatini Salles and Saulo Bortolon},
title={Automatic Identification of Mycobacterium tuberculosis in Ziehl-Neelsen Stained Sputum Smear Microscopy Images using a Two-stage Classifier},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005345201860191},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Automatic Identification of Mycobacterium tuberculosis in Ziehl-Neelsen Stained Sputum Smear Microscopy Images using a Two-stage Classifier
SN - 978-989-758-091-8
AU - Soares L.
AU - Coco K.
AU - Salles E.
AU - Bortolon S.
PY - 2015
SP - 186
EP - 191
DO - 10.5220/0005345201860191