Abstract
Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images, and any post-processing tasks may benefit from the reduction of noise. The major two noises in computed radiography (CR) images are Gaussian white noise and Poisson noise. By considering both the characteristics of CR images and the statistical features of wavelet transformed coefficients, an efficient spatial adaptive filtering algorithm, which is based on statistical model of local dependency of CR image wavelet coefficients and the approximate minimum mean squared error (MMSE) estimation, is proposed to decrease the Gaussian white noise in computed images. The process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).
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References
Chenad, T.J., Chuanga, K.S., Chiang, Y.C.: A Statistical Method for Evaluation Quality of Medical Images: A Case Study in Bit Discarding and Image Compression. Computerized Medical Imaging and Graphics 28, 167–175 (2004)
Wang, J.L., Wang, Y.P, Li, D.Y., et al.: CR Image Filter Methods Research Based on Wavelet-Domain Hidden Markov Models. Optical Information Processing. In: Sheng, Y., Zhuang, S., Zhang, Y. (eds.) Proceedings of the SPIE, vol. 60 , pp. 224–233 (2006)
Pierre, G., Gilles, B., De, G., Jacques, A.: A Method for Modeling Noise in Medical Images. IEEE Transactions On Medical Imaging 23, 1221–1232 (2004)
Donoho, D.L.: De-noising by Soft-thresholding. IEEE Transactions on Information Theory 41, 613–627 (1995)
Donoho, D.L., Johnstone, I.M.: Ideal Spatial Adaptation via Wavelet Shrinkage. Biometrika 81, 425–455 (1994)
Donoho, D.L., Johnstone, I.M.: Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Assoc. 90, 1200–1224 (1995)
Birge, L., Massart, P.: From Model Selection to Adaptive Estimation. In: Pollard, D., Yang, G. (eds.) Research Papers in Probability and Statistics: Festschrift for Lucien Le Cam, pp. 55–88. Springer, New York (1996)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive Wavelet Thresholding for Image Denoising and Compression. IEEE Transactions on Image Processing 9, 1532–1546 (2000)
Crouse, M., Nowak, R., Baraniuk, R.: Wavelet-based Statistical Signal Processing Using Hidden Markov Models. IEEE Transactions on Signal Processing 42, 886–902 (1998)
Shapiro, J.M.: Embedded Image Coding Using Zerotrees of Wavelet Coefficients. IEEE Transactions on Signal Processing 41, 3445–3462 (1993)
Mihscak, M.K., Kozintsev, I., Ramchandran, K., Moulin, P.: Low-complexity Image Denoising Based on Statistical Modeling of Wavelet Coefficients. IEEE Signal Processing Letter 6, 300–303 (1999)
Chang, S.G., Yu, B., Vetterli, M.: Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Transactions on Image Processing 9, 1522–1531 (2000)
Mihscak, M.K., Kozintsev, I., Ramchandran, K.: Spatially Adaptive Statistical Modeling of Wavelet Image Coefficients and Its Application to Denoising. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3253-3256 (1999)
Donoho, D.L.: Denoising by Wavelet Thresholding. IEEE Trans. Inform. Theory 41, 613–627 (1995)
Xie, J.C., Zhang, D.L., Xu, W.L.: Spatially Adaptive Wavelet Denoising Using the Minimum Description Length Principle. IEEE Transactions on Image Processing 13, 179–187 (2004)
Levent, S., Selesnick, I.W.: Bivariate Shrinkage Functions for Wavelet Based Denoising Exploiting Interscale Dependency. IEEE Trans. Signal Processing 12, 2744–2756 (2002)
Rahman, S.M., Hasan, M.K.: Wavelet-domain Iterative Center Weighted Median Filter for Image Denoising. Signal Processing 83, 1001–1012 (2003)
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Hua, Cj., Chen, Y. (2007). Wavelet-Based CR Image Denoising by Exploiting Inner-Scale Dependency. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_99
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DOI: https://doi.org/10.1007/978-3-540-74171-8_99
Publisher Name: Springer, Berlin, Heidelberg
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