Hybrid Filter Based on Neural Networks for Removing Quantum Noise in Low-Dose Medical X-ray CT Images

Park, Keunho;Lee, Hee-Shin;Lee, Joonwhoan

  • 투고 : 2015.05.09
  • 심사 : 2015.05.27
  • 발행 : 2015.09.30


The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.


Neural network;Noise removal;Quantum noise;Bilateral filters;Neural edge enhancer


  1. H. Hanek and N. Ansari, “Speeding up the generalized adaptive neural filters,” IEEE Trans. Image Processing, 5: 705-712, 1996.
  2. K. Arakawa and H. Harashima, “A nonlinear digital filter using multilayered neural networks,” Proc. IEEE Int. Conf. Commum., 2: 424-428, 1990.
  3. K. Suzuki, Isao Horiba and Noboru Sugie, “Efficient approximation of neural filters for removing quantum noise from images,” IEEE Trans. on Signal Processing, 50:1787-1799, 2002.
  4. K. M. Hornik, “Approximation capabilities of multilayer feed forward networks are universal,” Neural Networks, 4: 251-257, 1991.
  5. Tomasi Carlo, “Bilateral filtering for gray and color images,” Proceedings of the 1998 IEEE Int. Conf. on Computer Vision, Bombay 1998.
  6. K. Suzuki, I. Horiba and N. Sugie, “Neural Edge Enhancer for Supervised enhancement from noisy images,” IEEE Trans. on PAMI, 25: 1582-1596, 2003.
  7. M. E. Y?ksel, “A Hybrid Neuro-Fuzzy Filter for Edge Preserving Restoration of Images Corrupted by Impulse Noise,” IEEE Trans. on Image Processing, 15(4): 928-936, 2006.
  8. M. R. Banham and Katsaggelos, A. K., “Digital image restoration,” IEEE Signal Processing Magazine, 14(2): 965-970, 1997.
  9. G. Cincotti, Loi. Giovanna and M. Pappalardo, “Frequency decomposition and compounding of ultrasound medical images with wavelet packets,” IEEE Trans. on Medical Imaging, 20(8): 764-771, 2001.
  10. A. Borsdorf, R. Raupach, T. Flohr and J. Hornegger, “Wavelet based noise reduction in CT images using correlation analysis,” IEEE Trans. on Medical Imaging, 27: 1685-1703, 2008.
  11. I. Elbakri and J. Fessler, “Efficient and accurate likelihood for iterative image reconstruction in X-ray computed tomography,” Proceedings of the SPIE. Conf. on Medical Imaging(Image Processing), 5032: 1839-1850, 2003.
  12. J. Fessler and K. Lange, “Grouped coordinate ascent algorithms for penalized likelihood transmission image reconstruction,” IEEE Trans. on Medical Imaging, 16: 166-175, 1997.
  13. M. Kachelrieb, O. Watzke and W. A. Kalender, “Generalized multi-dimensional adaptive filtering for conventional and spiral single slice, multi slice, and cone beam CT,” Medical Physics, 28: 475-490, 2001.
  14. H. Lu, X. Li, D. Chen, Y. Xing, J. Hsieh and Z. Liang, “Adaptive noise reduction toward low dose computed tomography,” Proceedings of the SPIE. Conf. on Medical Imaging (Physics of Medical Imaging), 5030: 759-766, 2003.
  15. A. Mouton, G. T. Flitton, S. Bizot, N. Megherbi and T. P. Breckon, “An evaluation of image denoising techniques applied to CT baggage screening imagery,” IEEE Int. Conference on Industral Technology, 25-28, 2013.
  16. S. Devi and J. Cheriyan, “Image enhancement using guided image filter and wavelet-based edge detection,” Int. Journal of Modern Engineering Research, 3: 1702-1706, 2012.
  17. A. Buades, B. Coll and J.-M. Morel, “A Non-Local Algorithm for Image Denoising,” Proceedings of CVPR-05, 2: 60-65, 2005.
  18. P. Coupe, P. Hellier, C. Kervrann and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Trans. on Image Processing, 18: 2221–2229, 2009.
  19. D. H. Trinh, M. Luong, J. Rocchisani, C. D. Pham, H. D. Pham and F. Dibos, “An optimal weight method for CT image denoising,” Journal of Electronic and Technology, 10: 124-129, 2012.
  20. C. Suess and X. Y. Chen, “Dose optimization in pediatric CT: Current technology and future innovations,” Pediatric Radiology, 32: 729-734, 2002.

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