Mammographic Images Enhancement and Denoising for Microcalbfication Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. In this paper, we present an algorithm for mammographic images enhancement and denoising based on the wavelet transform. In particular, we develop an adaptive procedure to perform an optimal denoising using a local iterative fuzzy noise variance estimation. Moreover, the degree of enhancement is adoptively tuned at each scale. The proposed algorithm has been tested on clinical images

[1]  Metin Akay,et al.  A Discrete Dyadic Wavelet Transform for Multidimensional Feature Analysis , 1998 .

[2]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[3]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[4]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  L. Costaridou,et al.  A wavelet-based spatially adaptive method for mammographic contrast enhancement , 2003, Physics in medicine and biology.

[6]  A. Aldroubi,et al.  Wavelets in Medicine and Biology , 1997 .

[7]  Andrew F. Laine,et al.  Wavelets for contrast enhancement of digital mammography , 1995 .

[8]  M. Vetterli,et al.  Wavelets, subband coding, and best bases , 1996, Proc. IEEE.

[9]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[10]  Arianna Mencattini,et al.  Wavelet based adaptive algorithm for mammographic images enhancement and denoising , 2005, IEEE International Conference on Image Processing 2005.

[11]  D. Donoho,et al.  Redundant Multiscale Transforms and Their Application for Morphological Component Separation , 2004 .

[12]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[13]  Jian Fan,et al.  Contrast enhancement by dyadic wavelet analysis , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[15]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  F. Hampel.  The Influence Curve and Its Role in Robust Estimation , 1974 .

[17]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[18]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[19]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[20]  Andrew F. Laine,et al.  Hexagonal wavelet representations for recognizing complex annotations , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Andrew F. Laine,et al.  Contrast Enhancement by Multiscale and Nonlinear Operators , 1996 .