Dose Reduction by the Use of a Wavelet-Based Denoising Method for Digital Radiography

Read  full  paper  at:http://www.scirp.org/journal/PaperInformation.aspx?PaperID=54111#.VO2BbCzQrzE

The primary purpose of this paper is to provide a novel wavelet-domain method for digital radiography with low dose examination. Approach of this study is an improved wavelet-transform-based method for potentially reducing radiation dose while maintaining clinically acceptable image quality. The wavelet algorithm integrates the advantages of wavelet-coefficient-weighted method and the existing Bayes Shrink thresholding method. In order to confirm the usefulness of the proposed method, the resolving and noise characteristics of the processed computed radiography images were measured. In addition, variations of contrast and noise with respect to radiation dose were also examined. Finally, to verify the effect of clinical examination, visual evaluations were also performed in lower abdominal area using phantom. Our quantitative results demonstrated that our wavelet algorithm could improve resolution characteristics while keeping the noise level within acceptable limits. Visual evaluation result demonstrated that the proposed method was superior to other published methods. Our proposed method recognized effect on decreasing in exposure dose in lower abdominal radiographs. As a conclusion, our proposed method’s performance is better when compared with that of the 3 conventional methods. The proposed method has the potential to improve visibility in radiographs when a lower radiation dose is applied.

Cite this paper

Watanabe, H. , Tsai, D. , Lee, Y. and Matsuyama, E. (2015) Dose Reduction by the Use of a Wavelet-Based Denoising Method for Digital Radiography. Health, 7, 220-230. doi: 10.4236/health.2015.72026.

References

[1] Gruber, M., Weber, M., Homolka, P., Nemec, S., Fruehwald-Pallamar, J. and Uffmann, M. (2011) Feasibility of Dose Reduction Using Needle-Structured Image Plates versus Powder-Structured Plates for Computed Radiography of the Knee. American Journal of Roentgenology, 197, 318-323.
http://dx.doi.org/10.2214/AJR.10.5505
[2] Liu, X., Shaw, C.C., Lai, C.J. and Wang, T. (2011) Comparison of Scatter Rejection and Low-Contrast Performance of Scan Equalization Digital Radiography (SEDR), Slot-Scan Digital Radiography, and Full-Field Digital Radiography Systems for Chest Phantom Imaging. Medical Physics, 38, 23-33.
http://dx.doi.org/10.1118/1.3519903
[3] Schaefer-Prokop, C., Neitzel, U., Venema, H.W., Uffmann, M. and Prokop, M. (2008) Digital Chest Radiography: An Update on Modern Technology, Dose Containment and Control of Image Quality. European Journal of Radiology, 18, 1818-1830.
http://dx.doi.org/10.1007/s00330-008-0948-3
[4] Donoho, D.L. (1995) De-Noising by Soft-Thresholding. IEEE Transactions on Information Theory, 41, 613-627.
http://dx.doi.org/10.1109/18.382009
[5] Jansen, M., Uytterhoeven, G. and Bultheel, A. (1999) Image De-Noising by Integer Wavelet Transforms and Generalized Cross Validation. Medical Physics, 26, 622-630.
http://dx.doi.org/10.1118/1.598562
[6] Harpan, M.D. (1999) A Computer Simulation of Wavelet Noise Reduction in Computed Tomography. Medical Physics, 26, 1600-1606.
http://dx.doi.org/10.1118/1.598654
[7] Okamoto, T., Furui, S., Ichiji, H., Yoshino, S., Lu, J. and Yanagi, T. (2004) Noise Reduction in Digital Radiography Using Wavelet Packet Based on Noise Characteristics. Journal of Signal Processing, 8, 485-494.
http://dx.doi.org/10.2299/jsp.8.485
[8] Tischenko, O., Hoeschen, C. and Buhr, E. (2005) Reduction of Anatomical Noise in Medical X-Ray Images. Radiation Protection Dosimetry, 114, 69-74.
http://dx.doi.org/10.1093/rpd/nch518
[9] Ferrari, R.J. and Winsor, R. (2005) Digital Radiographic Image Denoising via Wavelet-Based Hidden Markov Model Estimation. Journal of Digital Imaging, 18, 154-167.
http://dx.doi.org/10.1007/s10278-004-1908-3
[10] Yasuda, N., Ishikawa, Y. and Kodera, Y. (2005) Improvement of Image Quality in Chest MDCT Using Nonlinear Wavelet Shrinkage with Trimmed-Thresholding. Japanese Journal of Radiological Technology, 61, 1599-1608.
[11] Lee, Y., Tsai, D.Y. and Suzuki, T. (2008) Contrast Enhancement of Medical Images Using Sigmoid-Type Transfer Curves for Wavelet Coefficient Weighting Adjustment. Medical Imaging and Information Sciences, 25, 48-53.
[12] Tsai, D.Y. and Lee, Y. (2003) A Method of Medical Image Enhancement Using Wavelet Coefficient Mapping Functions. Proceedings of IEEE International Conference on Neural Networks and Signal Processing, 2, 1091-1094.
[13] Watanabe, H., Tsai, D.Y., Lee, Y., Matsuyama, E. and Kojima, K. (2011) An Integrated Method of Wavelet Coefficient Thresholding for Reducing Radiation Dose While Maintaining Diagnostic Image Quality. Medical Imaging and Information Sciences, 28, 51-56.
[14] Chang, S.G., Yu, B. and Vetterli, M. (2000) Adaptive Wavelet Thresholding for Image Denoising and Compression. IEEE Transactions on Image Processing, 9, 1532-1546.
http://dx.doi.org/10.1109/83.862633
[15] Donoho, D.L. and Johnstone, J.M. (1994) Ideal Spatial Adaptation via Wavelet Shrinkage. Biometrika, 81, 425-455.
http://dx.doi.org/10.1093/biomet/81.3.425
[16] Donoho, D.L. and John-stone, I.M. (1995) Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Association, 90, 1200-1224.
http://dx.doi.org/10.1080/01621459.1995.10476626
[17] Zhang, M. and Gunturk, B.K. (2008) Multiresolution Bilateral Filtering for Image Denoising. IEEE Transactions on Image Processing, 17, 2324-2333.
http://dx.doi.org/10.1109/TIP.2008.2006658
[18] Sendur, L. and Selesnick, I.W. (2002) Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency. IEEE Transactions on Signal Processing, 50, 2744-2756.
http://dx.doi.org/10.1109/TSP.2002.804091
[19] Karthikeyan, K. and Chandrasekar, C. (2011) Speckle Noise Reduction of Medical Ultrasound Images Using Bayesshrink Wavelet Threshold. International Journal of Computer Applications, 22, 8-14.
http://dx.doi.org/10.5120/2614-3646
[20] Watanabe, H., Tsai, D.Y., Lee, Y., Nakamura, T., Miyazaki, M., Kuramochi, Y. and Kojima, K. (2009) Evaluation of Irreversible Compressed Images in Computed Radiography Using Physical Image Quality Measures. Japanese Journal of Radiological Technology, 65, 1618-1627.
http://dx.doi.org/10.6009/jjrt.65.1618
[21] Samei, E., Flynn, M.J. and Reimann, D.A. (1998) A Method for Measuring the Presampled MTF of Digital Radiographic Systems Using an Edge Test Device. Medical Physics, 25, 102-113.
http://dx.doi.org/10.1118/1.598165
[22] Flynn, M. and Samei, E. (1999) Experimental Comparison of Noise and Resolution for 2k and 4k Storage Phosphor Radiography Systems. Medical Physics, 26, 1612-1623.
http://dx.doi.org/10.1118/1.598656
[23] Neitzel, U., Gunther-Kohfahl, S., Borasi, G. and Samei, E. (2004) Determination of Detective Quantum Efficiency of a Digital X-Ray Detector: Comparison of Three Evaluations Using a Common Image Data Set. Medical Physics, 31, 2205-2211.
http://dx.doi.org/10.1118/1.1766421
[24] Samei, E. and Flynn, M.J. (2002) An Experimental Comparison of Detector Performance for Computed Radiography Systems. Medical Physics, 29, 447-459.
http://dx.doi.org/10.1118/1.1449873
[25] Bankman, I.N. (2000) Handbook of Medical Imaging. Academic Press, San Diego, 24-26.         eww150225lx
[26] Lim, J.S. (1989) Two-Dimensional Signal and Image Processing. Prentice Hall, Englewood Cliffs, 548.
Advertisements

发表评论

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / 更改 )

Twitter picture

You are commenting using your Twitter account. Log Out / 更改 )

Facebook photo

You are commenting using your Facebook account. Log Out / 更改 )

Google+ photo

You are commenting using your Google+ account. Log Out / 更改 )

Connecting to %s