Design of New Volterra Filter for Mammogram Enhancement

Non-linear filters are generally preferred for image enhancement applications as they provide better filtering results not only by suppressing background noise but also preserving the edges. This paper introduces a new technique for enhancement of digital mammograms using a Volterra filter. The proposed Volterra filter design is obtained by truncation of Volterra series to the first non-linear terms. Truncation of Volterra series leads to a simpler and effective representation without having prior knowledge of higher order statistics. The weight indices of the proposed filter are optimally selected in a manner to provide better enhancement of lesions in the mammograms in comparison to other techniques.

[1]  Sven Loncaric.  IWISPA 2000 : proceedings of the First International Workshop on Image and Signal Processing and Analysis : in conjunction with 22nd International Conference on Information Technology Interfaces : Pula, Croatia, June 14-15, 2000 , 2000 .

[2]  Yicong Zhou,et al.  Mammogram enhancement using alpha weighted quadratic filter , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Sadeer G. Al-Kindi,et al.  Breast sonogram and mammogram enhancement using hybrid and repetitive smoothing-sharpening technique , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[4]  G. Sicuranza.  Quadratic filters for signal processing , 1992, Proc. IEEE.

[5]  K. Preston,et al.  Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs , 1988, IEEE Transactions on Biomedical Engineering.

[6]  Olac Fuentes,et al.  Super-resolution of mammograms , 2010, 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[7]  V. J. Mathews.  Adaptive polynomial filters , 1991, IEEE Signal Processing Magazine.

[8]  Jean B. Cormack,et al.  Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST. , 2008, Radiology.

[9]  Ed X. Wu,et al.  Realizations of fast 2-D/3-D image filtering and enhancement , 2001, IEEE Transactions on Medical Imaging.

[10]  Nor Ashidi Mat Isa,et al.  A proposed system for edge mammogram image , 2010 .

[11]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[12]  G. Ramponi.  Bi-impulse response design of isotropic quadratic filters , 1990 .

[13]  Bradley M. Hemminger,et al.  Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms , 1998, Journal of Digital Imaging.

[14]  A. Venetsanopoulos,et al.  Order statistics in digital image processing , 1992, Proc. IEEE.

[15]  G. Arce,et al.  Morphological filters: Statistics and further syntactic properties , 1987 .

[16]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .