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Design of New Volterra Filter for Mammogram Enhancement

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

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.

Keywords

Isotropic Mammograms MIAS Database Symmetric Volterra Filter 

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References

  1. 1.
    Pisano, E.D., Hendrick, E., et al.: Diagnostic Accuracy of Digital versus Film Mammography: Exploratory Analysis of Selected Population Subgroups in DMIST. Radiology, 376–383 (2008)Google Scholar
  2. 2.
    Tang, H., Zhuang, T., Wu, E.X.: Realization of Fast 2-D/3-D Image Filtering and Enhancement. IEEE Transactions on Medical Imaging 20(2), 132–140 (2001)CrossRefGoogle Scholar
  3. 3.
    Jusman, Y., Isa, N.A.M.: A Proposed System for Edge Mammogram Image. In: Proc. of the 9th World Scientific and Engineering Academy and Society (WSEAS) International Conference on Applications of Electrical Engineering, pp. 117–123 (2010)Google Scholar
  4. 4.
    Zheng, J., Fuentes, O., Leung, M.: Super Resolution of Mammograms. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Montreal, pp. 1–7 (2010)Google Scholar
  5. 5.
    Al-Kindi, S.G., Al-Kindi, G.A.: Breast Sonogram and Mammogram Enhancement Using Hybrid and Repetitive Smoothing-Sharpening Technique. In: Proc. of the 1st Middle East Conference on Biomedical Engineering (MECBME), Sharjah, pp. 446–449 (2011)Google Scholar
  6. 6.
    Mathews, V.J., Sicuranza, G.L.: Volterra and General Volterra Related Filtering. In: IEEE Winter Workshop on Nonlinear Digital Signal Processing, pp. T_2.1–T_2.8 (1993)Google Scholar
  7. 7.
    Salmond, D.J., et al.: Novel Approach to Non-linear/Non-Gaussian Bayesian State Estimation. In: Proc. of the IEEE on Radar and Signal Processing, vol. 140, pp. 107–113 (1993)Google Scholar
  8. 8.
    Pitas, I., Venetsanopoulos, A.N.: Order Statistics in Digital Image Processing. Proc. of the IEEE 80(12), 1893–1921 (1992)CrossRefGoogle Scholar
  9. 9.
    Stevenson, R., Arce, G.: Morphological Filters: Statistics and Further Syntactic Properties. IEEE Transactions on Circuits and Systems 34(11), 1292–1305 (1987)CrossRefGoogle Scholar
  10. 10.
    Mathews, V.J.: Adaptive Volterra Filter. IEEE Signal Processing Magazine 8(3), 10–26 (1991)CrossRefGoogle Scholar
  11. 11.
    Sicuranza, G.L.: Quadratic Filters for Signal Processing. Proc. of the IEEE 80(8) (1992)Google Scholar
  12. 12.
    Sicuranza, G.L.: Volterra Filters for Image and Video Processing. In: Proc. of the First International Workshop on Image and Signal Processing and Analysis, Pula, pp. 15–26 (2000)Google Scholar
  13. 13.
    Morrow, W.M., et al.: Region Based Contrast Enhancement of Mammograms. IEEE Transactions on Medical Imaging 11, 392–406 (1992)CrossRefGoogle Scholar
  14. 14.
    Suckling, J., et al.: The Mammographic Image Analysis Society Mammogram Database. In: Proc. of 2nd Int. Workshop Digital Mammography, York, U.K., pp. 375–378 (1994)Google Scholar
  15. 15.
    Rogowska, J., Preston, K., Shasin, D.: Evaluation of Digital Unsharp Masking and Local Contrast Stretching as Applied to Chest Radiology. IEEE Transactions on Information Technology in Biomedical Engineering 35(2), 236–251 (2009)Google Scholar
  16. 16.
    Pisano, E.D., et al.: Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms. Journal of Digital Imaging 11, 193–200 (1998)CrossRefGoogle Scholar
  17. 17.
    Ramponi, G.: Bi-impulse Response Design of Isotropic Quadratic Filters. Proc. of the IEEE 78(4), 665–667 (1990)CrossRefGoogle Scholar
  18. 18.
    Zhou, Y., et al.: Mammogram Enhancement Using Alpha Weighted Quadratic Filter. In: Proc. of Annual International Conf. IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, pp. 3681–3684 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ashutosh Pandey
    • 1
  • Anurag Yadav
    • 1
  • Vikrant Bhateja
    • 1
  1. 1.Deptt. of Electronics and Communication EngineeringShri Ramswaroop Memorial Group of Professional CollegesLucknowIndia

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