Abstract
Digital mammographic image processing often requires a previous application of filters to reduce the noise level of the image while preserving important details. This may improve the quality of digital mammographic images and contribute to an accurate diagnosis. Denoising methods based on linear filters cannot preserve image structures such as edges in the same way that methods based on nonlinear filters can do it. Recently, a nonlinear denoising method based on ICA has been introduced [1,2] for natural and artificial images. The functioning of the ICA denoising method depends on the statistics of the images. In this paper, we show that mammograms have statistics appropriate for ICA denoising and we demonstrate experimentally that ICA denoising is a suitable method to remove the noise of digitised mammographys.
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References
Hyvärinen, A.: Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation. Neural Computation 11 (7), 1739–1768 (1999)
Hyvärinen, A., Hoyer, P., Oja, E.: Image Denoising by Sparse Code Shrinkage. In: Hykin, S., Kosko, B. (eds.) Intelligent Signal Processing, IEEE Press, Los Alamitos (2001)
Dengler, J., Behrens, S., Desaga, J.F.: Segmentation of microcalcifications in mammograms. IEEE Transactions on Medical Imaging 12, 634–642 (1993)
Donoho, D., Johnstone, I., Kerkyacharian, G., Picard, D.: Wavelet Shrinkage: Asymptopia? Journal of the Royal Statistical Society 57, 301–369 (1995)
Catté, F., Lions, P., Morel, J., Coll, T.: Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. SIAM Numerical Analysis 29, 182–193 (1992)
Hyvärinen, A., Oja, E.: Independent Component Analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)
Hoyer, P.: Independent component analysis of image denoising, Master’s Thesis, Helsinki University of Technology (1999)
Comon, P.: Independent component analysis–a new concept? Signal Processing 36, 287–314 (1994)
Hurri, J., Hyvärinen, A., Oja, E.: Wavelets and natural image statistics. In: Frydrych, M., Parkinnen, J., Visa, A. (eds.) Scandinavian Conference on Image Analysis, Finland (1997)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10 (3), 626–634 (1999)
Sakellaropoulos, P., Costaridou, L., Pabayiotakis, G.: A wavelet-based spatially adaptive method for mammographic contrast enhancement. Physics in Medicine and Biology 48 (6), 787–803 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Mayo, P., Rodenas Escriba, F., Verdú Martín, G. (2004). Denoising Mammographic Images Using ICA. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_134
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DOI: https://doi.org/10.1007/978-3-540-30110-3_134
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