Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Fast non-local means noise reduction algorithm with acceleration function for improvement of image quality in gamma camera system: A phantom study

  • Park, Chan Rok (Department of Nuclear Medicine, Seoul National University Hospital) ;
  • Lee, Youngjin (Department of Radiological Science, Gachon University)
  • Received : 2018.08.09
  • Accepted : 2018.12.20
  • Published : 2019.04.25
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Abstract

Gamma-ray images generally suffer from a lot of noise because of low photon detection in the gamma camera system. The purpose of this study is to improve the image quality in gamma-ray images using a gamma camera system with a fast nonlocal means (FNLM) noise reduction algorithm with an acceleration function. The designed FNLM algorithm is based on local region considerations, including the Euclidean distance in the gamma-ray image and use of the encoded information. To evaluate the noise characteristics, the normalized noise power spectrum (NNPS), contrast-to-noise ratio (CNR), and coefficient of variation (COV) were used. According to the NNPS result, the lowest values can be obtained using the FNLM noise reduction algorithm. In addition, when the conventional methods and the FNLM noise reduction algorithm were compared, the average CNR and COV using the proposed algorithm were approximately 2.23 and 7.95 times better than those of the noisy image, respectively. In particular, the image-processing time of the FNLM noise reduction algorithm can achieve the fastest time compared with conventional noise reduction methods. The results of the image qualities related to noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in a gamma camera system.

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References

  1. E.G. DePuey, J. Nucl. Cardiol. 19 (2012) 1085. https://doi.org/10.1007/s12350-012-9597-7
  2. H.C. Kim, H.J. Kim, K. Kim, M.H. Lee, Y. Lee, Nucl. Eng. Technol. 49 (2017) 776. https://doi.org/10.1016/j.net.2016.12.002
  3. C. Scheiber, Nucl. Instrum. Methods Phys. Res. 448 (2000) 513. https://doi.org/10.1016/S0168-9002(00)00282-5
  4. F.J. Beekman, B. Vastenhouw, Phys. Med. Biol. 49 (2004) 4579. https://doi.org/10.1088/0031-9155/49/19/009
  5. H.O. Anger, J. Nucl. Med. 5 (1964) 515.
  6. Y. Lee, H.J. Kim, Nucl. Instrum. Methods Phys. Res. 794 (2015) 54. https://doi.org/10.1016/j.nima.2015.05.007
  7. O. Gal, B. Dessus, F. Jean, F. Laine, C. Leveque, IEEE Trans. Nucl. Sci. 48 (2001) 1198. https://doi.org/10.1109/23.958750
  8. S.H. Kim, K. Seo, S.H. kang, S. Bae, H.J. Kwak, J.W. Hong, Y. Hwang, S.M. Kang, H.R. Choi, G.Y. Kim, Y. Lee, J. Magnetics 22 (2017) 570. https://doi.org/10.4283/JMAG.2017.22.4.570
  9. P. Chatterjee, P. Milanfar, IEEE Trans. Nucl. Sci. 19 (2010) 895.
  10. E. Arias-Castro, D.L. Donoho, Ann. Stat. 37 (2009) 1172. https://doi.org/10.1214/08-AOS604
  11. X. Zhang, Optik 127 (2016) 6821. https://doi.org/10.1016/j.ijleo.2016.05.002
  12. L.I. Rudin, S. Osher, E. Fatemi, Physica D 60 (1992) 259. https://doi.org/10.1016/0167-2789(92)90242-F
  13. Y.M. Huang, M.K. Ng, Y.W. Wen, SIAM J. Imag. Sci. 2 (2009) 20. https://doi.org/10.1137/080712593
  14. K. Seo, S.H. Kim, S.H. Kang, J. Park, C.L. Lee, Y. Lee, J. Magnetics 21 (2016) 593. https://doi.org/10.4283/JMAG.2016.21.4.593
  15. A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denoising, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, https://doi.org/10.1109/CVPR.2005.38.
  16. A. Efros, T. Leung, in: Proceeding of IEEE International Conference on Computer Vision, vol. 2, 1999, p. 1033.
  17. J. Wang, Y. Guo, Y. Ying, Y. Liu, Q. Peng, Fast non-local algorithm for image denoising, in: IEEE International Conference on Image Processing, 2006, https://doi.org/10.1109/ICIP.2006.312698.
  18. J.T. Dobbins III, D.L. Ergun, L. Rutz, D.A. Hinshaw, H. Blume, D.C. Clark, Med. Phys. 22 (1995) 1581. https://doi.org/10.1118/1.597627
  19. J.T. Dobbins III, E. Samei, N.T. Ranger, Y. Chen, Med. Phys. 33 (2006) 1466. https://doi.org/10.1118/1.2188816

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