Journal of Magnetics

The Korean Magnetics Society (한국자기학회)
권호 표지
DOI QR코드

DOI QR Code

The Effects of Total Variation (TV) Technique for Noise Reduction in Radio-Magnetic X-ray Image: Quantitative Study

  • Seo, Kanghyen (Department of Radiological Science, Eulji University) ;
  • Kim, Seung Hun (Department of Radiological Science, Eulji University) ;
  • Kang, Seong Hyeon (Department of Radiological Science, Eulji University) ;
  • Park, Jongwoon (Department of Radiological Science, Eulji University) ;
  • Lee, Chang Lae (Department of Radiological Science, Yonsei University) ;
  • Lee, Youngjin (Department of Radiological Science, Eulji University)
  • Received : 2016.09.15
  • Accepted : 2016.12.15
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In order to reduce the amount of noise component in X-ray imaging system, various reduction techniques were frequently used in the field of diagnostic imaging. Although the previous techniques -such as median, Wiener filters and Anscombe noise reduction technique - were able to reduce the noise, the edge information was still damaged. In order to cope with this problem, total variation (TV) noise reduction technique has been developed and researched. The purpose of this study was to evaluate and compare the image quality using normalized noise power spectrum (NNPS) and contrast-to-noise ratio (CNR) through simulations and experiments with respect to the above-mentioned noise reduction techniques. As a result, not only lowest NNPS value but also highest CNR values were acquired using a TV noise reduction technique. In conclusion, the results demonstrated that TV noise reduction technique is proved as the most practical method to ensure accurate denoising in X-ray imaging system.

Keywords

References

  1. D. R. K. Brownrigg, Communications of the ACM 27, 807 (1984). https://doi.org/10.1145/358198.358222
  2. T. Chen, K. K. Ma, and L. H Chen, IEEE Transactions on Image Processing 8, 1834 (1999). https://doi.org/10.1109/83.806630
  3. A. Somkuwar and S. Bhargava, 2nd International Conference on Mechanical, Electronics and Mechatronics Engineering (2013) pp. 115-119.
  4. M. Makitalo and A. Foi, IEEE International Conference on Acoustics Speech and Signal Processing (2012) pp. 1081-1084.
  5. M. Makitalo and A. Foi, IEEE Transaction on Image Processing 20, 2697 (2011). https://doi.org/10.1109/TIP.2011.2121085
  6. J. Chen, J. Benesty, Y. Huang, and S. Doclo, IEEE Transaction on Audio, Speech and Language Processing 14, 1218 (2006). https://doi.org/10.1109/TSA.2005.860851
  7. Q. Chen, P. Montesinos, Q. S. Sun, P. A. Heng, and D. S. Xia, Image and Vision Computing 28, 298 (2010). https://doi.org/10.1016/j.imavis.2009.04.012
  8. A. Buades, B. Coll, and J. M. Morel 4, 490 (2005). https://doi.org/10.1137/040616024
  9. C. R. Vogel and M. E. Oman, SIAM Journal on Scientific Computing 17, 227 (1996). https://doi.org/10.1137/0917016
  10. C. Tomasi and R. Manduchi, IEEE International Conference on Computer Vision (1998) pp. 839-846.
  11. W. Dong, L. Zhang, G. Shi, and X. Li, IEEE Transactions on Image Processing 22, 1620 (2013). https://doi.org/10.1109/TIP.2012.2235847
  12. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, IEEE Transactions on Image Processing 16, 2080 (2007). https://doi.org/10.1109/TIP.2007.901238
  13. V. N. P. Raj, IEEE Transaction on Recent Advances in Intelligent Computational Systems (RAICS) (2011) pp. 483-488.
  14. L. Rudin, S. Osher, and E. Fatemi, Physica D 60, 259 (1992). https://doi.org/10.1016/0167-2789(92)90242-F
  15. J. T. Dobbins III, E. Samei, N. T. Ranger, and Y. Chen, Medical Physics 33, 1466 (2006). https://doi.org/10.1118/1.2188816
  16. Y. N. Choi, S. W. Lee, H. M. Cho, H. J. Ryu, Y. J. Lee, and H. J. Kim, Journal of the Korean Physical Society 59, 3114 (2011). https://doi.org/10.3938/jkps.59.3114