Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Method for Identifying Tubercle Bacilli using Neural Networks

  • Lin, Sheng-Fuu (Department of Electrical and Control Engineering National Chiao Tung University) ;
  • Chen, Hsien-Tse (Department of Electrical and Control Engineering National Chiao Tung University)
  • Published : 2009.06.30
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Abstract

Phlegm smear testing for acid-fast bacilli (AFB) requires careful examination of tubercle bacilli under a microscope to distinguish between positive and negative findings. The biggest weakness of this method is the visual limitations of the examiners. It is also time-consuming, and mistakes may easily occur. This paper proposes a method of identifying tubercle bacilli that uses a computer instead of a human. To address the challenges of AFB testing, this study designs and investigates image systems that can be used to identify tubercle bacilli. The proposed system uses an electronic microscope to capture digital images that are then processed through feature extraction, image segmentation, image recognition, and neural networks to analyze tubercle bacilli. The proposed system can detect the amount of tubercle bacilli and find their locations. This paper analyzes 184 tubercle bacilli images. Fifty images are used to train the artificial neural network, and the rest are used for testing. The proposed system has a 95.6% successful identification rate, and only takes 0.8 seconds to identify an image.

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References

  1. M. G. Forero, F. Sroubek, and G. Cristobal, "Identification of tuberculosis bacteria based on shape and color," Real-Time Imaging, vol. 10, no. 4, pp. 251-262, 2004 https://doi.org/10.1016/j.rti.2004.05.007
  2. http://en.wikipedia.org/wiki/Ziehl-Neelsen_stain
  3. N. Otsu, "A threshold selection method from gray-level histogram," IEEE Trans. on Systems, Man and Cybernetics, vol.9, no. 1, pp. 62-66, 1979 https://doi.org/10.1109/TSMC.1979.4310076
  4. L. Liu and S. Sclaroff, "Medical image segmentation and retrieval via deformable models," Proc. IEEE Int. Conf. on Image Processing, vol. 3, pp. 1071-1074, 2001
  5. K. A. Marghani, S. S. Dlay, B. S. Sharif, and A. J. Sims, "Automated morphological analysis approach for classifying colorectal microscopic images," Proc. of SPIE, vol. 5267, pp. 249-249, 2003
  6. F. Ortiz, F. Torres, E. D. Juan, and N. Cuenca, "Colour mathematical morphology for neural image analysis", Real-Time Imaging, vol. 8, pp. 455-465, 2002 https://doi.org/10.1006/rtim.2002.0288
  7. K. Wu, D. Gauthier, and M. D. Levine, "Live Cell Image Segmentation," IEEE Trans, on Biomedical Engineering, vol. 42,pp. 1-12, 1995 https://doi.org/10.1109/10.362924
  8. J. R. Weaver and J. L Au, "Application of automatic thresholding in image analysis scoring of cells in human solid tumors labeled for proliferation markers," Cytometry A, vol. 29, pp. 128-135,1997 https://doi.org/10.1002/(SICI)1097-0320(19971001)29:2<128::AID-CYTO5>3.0.CO;2-9
  9. S. F. Lin and C. A. Hung, Introduction of Neural Networks and Pattern Recognition. Taiwan: OpenTech, 2002(In Chinese)
  10. Z. F. Zhang, L. C. Zhang, and H. L. Huang, Neural Network. Taiwan: OpenTech, 2003 (In Chinese)
  11. R. Grzeszczuk, D. Terzopoulos, and G. Hinton, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-Based Models. New York, ACM, 1998
  12. Q. Zheng, B. K. Milthorpe, and A. S. Jones, "Direct neural network application for automated cell recognition," Cytometry A, vol. 57, pp. 1-9, 2004