Noise Reduction in Medical X-Ray Images Using Wavelet and Neural Networks

Document Type : Original Article


1 Department of Electrical Engineering, Semnan University

2 Assistant professor, Department of Electrical Engineering, Adiban Institute of Higher Education, Garmsar, Iran


Noise reduction in x-rays has attracted many researcher in recent years. This noise is produced duo to reduction of X-ray powers. Increasing the X-ray powers threatens the human life. Wavelet domain threshold level proposed by many researchers for this noise reduction. However, the many cases this approach is invalid because the noise distribution function is poison. In this research study we used the wavelet domain based on a genetic algorithm to tune the BayesShrink threshold. To increase the clarity an image, we used a multi-layer perceptron neural network. However, this neural network and other approaches are not able to eliminate large amount of noises. To overcome this kind of noise, we used a Directional adaptive median filter. Because the edges of images may be vanished by this method, we reconstructed the edge level at the end of process. The simulation results indicate that our approach provides more clear images than many of other methods in the literature such as PSNR, MSR and CNR.


Okamoto, T., Furui, S., Ichiji, H., Yoshino, S., Lu, J., & Yahagi, T. (2004). Noise reduction in digital radiography using wavelet packet based on noise characteristics. Journal of Signal Processing, 8(6), 485–494. doi:10.2299/jsp.8.485
Nowak, R. D., & Baraniuk, R. G. (1999). Wavelet-domain filtering for photon imaging systems. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 8(5), 666–678. doi:10.1109/83.760334
Ozkan, M. K., Erdem, A. T., Sezan, M. I., & Tekalp, A. M. (1992). Efficient multiframe Wiener restoration of blurred and noisy image sequences. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 1(4), 453–476. doi:10.1109/83.199916
Wang, Z., & Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing, 46(1), 78–80. doi:10.1109/82.749102
Donoho, David L., Johnstone, I. M., Kerkyacharian, G., & Picard, D. (1995). Wavelet shrinkage: Asymptopia? Journal of the Royal Statistical Society, 57(2), 301–337. doi:10.1111/j.2517-6161.1995.tb02032.x
Atkinson, I., Kamalabadi, F., Mohan, S., & Jones, D. L. (2004). Wavelet-based 2-D multichannel signal estimation. Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429). Barcelona, Spain. doi:10.1109/icip.2003.1246636
Chang, S. G., Yu, B., & Vetterli, M. (2000). Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 9(9), 1532–1546. doi:10.1109/83.862633
Wang, L., Lu, J., Li, Y., Yahagi, T., & Okamoto, T. (2005). Noise reduction using wavelet with application to medical X-ray image. Στο 2005 IEEE International Conference on Industrial Technology (pp. 33–38). IEEE.
Kavchak, M. A., & Budman, H. M. (1998). Adaptive neural network architectures for nonlinear function estimation. Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207). Philadelphia, PA, USA. doi:10.1109/acc.1998.694629
Wang, G.,Guo, L., Duan,H., (2013). Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment,  Hindawi Publishing Corporation The ScientificWorld Journal,7 pages, doi:10.1155/2013/632437.
Chen, X.-L., Tian, M., & Yao, W.-B. (2005). GPR signals de-noising by using wavelet networks. 2005 International Conference on Machine Learning and Cybernetics. Guangzhou, China. doi:10.1109/icmlc.2005.1527766
Lotrič, U. (2004). Wavelet based denoising integrated into multilayered perceptron. Neurocomputing, 62, 179–196. doi:10.1016/j.neucom.2004.02.003
Zhang, X.-P. (2002). Space-scale adaptive noise reduction in images based on thresholding neural network. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221). UT, USA. doi:10.1109/icassp.2001.941313
Li, Y., Lu, J., Wang, L., & Takashi, Y. (2009). Denoising by using multineural networks for medical X-ray imaging applications. Neurocomputing, 72(13–15), 2884–2891. doi:10.1016/j.neucom.2008.07.019
Mastriani, M., & Giraldez, A. E. (2018). Microarrays denoising via smoothing of coefficients in wavelet domain.
Liu, P., & Li, H. (2002). Image restoration techniques based on fuzzy neural networks. Science in China Series F: Information Sciences, 45(4), 273–285.
Norouzzadeh, Y., & Katebi, S. D. (2006). Application of ANFIS in Wavelet Denoising. Στο 6th Iranian Conference on Fuzzy Systems and 1th Islamic World Conference on Fuzzy Systems، Islamic Azad University of Shiraz.
Norouzzadeh, Yaser, & Rashidi, M. (2011). Image denoising in wavelet domain using a new thresholding function. International Conference on Information Science and Technology. Nanjing, China. doi:10.1109/icist.2011.5765347
Norouzzadeh, Y., & Katebi, S. D. (2007). Threshold estimation in wavelet domain using fuzzy rules. Στο 4th Iranian Conference on Machine Vision and Image Processing.
Antoniadis, A., Bigot, J., & Sapatinas, T. (2001). Wavelet estimators in nonparametric regression: A comparative simulation study. Journal of statistical software, 6(6). doi:10.18637/jss.v006.i06
Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 41(3), 613–627. doi:10.1109/18.382009
Donoho, David L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3), 425–455. doi:10.1093/biomet/81.3.425
Donoho, David L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224. doi:10.1080/01621459.1995.10476626
Soltanian-Zadeh, H., Windham, J. P., & Yagle, A. E. (1995). A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 4(2), 147–161. doi:10.1109/83.342189
Cincotti, G., Loi, G., & Pappalardo, M. (2001). Frequency decomposition and compounding of ultrasound medical images with wavelet packets. IEEE Transactions on Medical Imaging, 20(8), 764–771. doi:10.1109/42.938244.