A Survey of Multiwavelet Filtering Based Denoising Techniques for Mammographic Medical Images
The digital mammographic imagery is often influenced by various types of noises and thus requires application of various filters to denoise the noise level in order to preserve the vital imagery contents. This evidently helps the medical practitioner to improve the image quality of the mammograms and helps them in giving accurate diagnosis. We have presented a survey of the popular denoising techniques used in the literature to achieve the same.
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Tamrakar S, Choubey A. A Survey of Multiwavelet Filtering Based Denoising Techniques for Mammographic Medical Images. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(1): 1-4
Dengler J, Behrens S, Desaga JF. Segmentation of microcalcifications in mammograms. IEEE Trans. on Medical Imaging. 1993; 12: 634–42p.
Donoho D, Johnstone I, Kerkyacharian G, et al. Wavelet shrinkage: asymptopia? Journal of the Royal Statistical Society B. 1995; 57: 301–69p.
Catté F, Lions P, Morel J, et al. Image selective smoothing and edge detection by nonlinear diffusion. SIAM Numerical Analysis. 1992; 29: 182–93p.
Hyvärinen A. Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation. Neural Comput. 1999; 11: 1739–68 p.
Hyvärinen A, Hoyer P, Oja E. Image denoising by sparse code shrinkage. In: Intelligent Signal Processing, S. Hykin, B. Kosko, Eds. IEEE Press, 2001.
Hoyer P. Independent component analysis of image denoising. Master's Thesis, Helsinki University of Technology, 1999.
Mallat S. A wavelet tour of signal processing. Academic Press, New York, 1998.
Hyvärinen A, Karhunen J, Oja E. Independent component analysis. In: Wiley series on Adaptive and Learning Systems for Signal Processing Communications and Control. S. Haykin Ed. Wiley, New York, 2001.
Comon P. Independent component analysis––a new concept? Signal Process. 1994; 36: 287–314p.
Mayo P, Rodenas F, Verdú G. Denoising mammographic images using ICA. Preprint of the Department of Nuclear and Chemical Engineering, Polytechnic University of Valencia (Spain), 2004 (unpublished).
Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks. 1999; 10: 626–34p.
Yang R, Yin L, Gabbouj M, et al. Optimal weighted median filters under structural constraints. IEEE Trans. Signal Processing. 1995; 43: 591–604 pp.
Hardie RC, Barner KE. Rank conditioned rank selection filters for signal restoration. IEEE Trans. Image Processing. 1994; 3:192–206p.
Ben Hamza A, Luque P, Martinez J, et al. Removing noise and preserving details with relaxed median filters. J. Math. Imag. Vision. 1999; 11(2): 161–77p.
Jain AK. Fundamentals of digital image processing. Prentice-Hall, 1989G.
Donoho DL, Johnstone IM. Ideal spatial adaption via wavelet shrinkage. Biometrika. 1994; 81: 425–455p.
Donoho DL, Johnstone IM. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association. 1995; 90(432): 1200–24p. National Laboratory, July 27, 2001.
Simoncelli EP, Adelson EH. Noise removal via Bayesian wavelet coring. In: Third Int'l Conf on Image Proc; 1996; I: 379–82p, Lausanne, September. IEEE Signal Proc Society.
Chipman HA, Kolaczyk ED, McCulloch RE. Adaptive Bayesian wavelet shrinkage. J. Amer. Stat. Assoc. 1997; 92(440): 1413–21p.
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