• DocumentCode
    624108
  • Title

    Contrast enhancement mammograms using denoising in wavelet coefficients

  • Author

    Kidsumran, Varakorn ; Chiracharit, W.

  • Author_Institution
    Dept. of Electron. & Telecommun. Eng., King Mongkut´s Univ. of Technol. Thonburi, Bangkok, Thailand
  • fYear
    2013
  • fDate
    29-31 May 2013
  • Firstpage
    82
  • Lastpage
    86
  • Abstract
    Contrast enhancement in x-ray mammograms is important in improvement of radiologists´ reading and interpretation. In many cases, it is difficult to discern signs of breast cancer because mammograms are low contrast and very noisy. This paper proposes improved contrast enhancement method in mammograms using denoising in wavelet coefficients. First, mammograms are decomposed by wavelet transform. Second, the ratio between approximate subband and detail subband as signal-to-noise ratio is computed. Finally, detail subbands which have the ratio lower than the criteria are boosted to improve mammograms contrast while detail subbands having the ratio higher than the criteria are set to zero for noise suppression in the same time. Contrast measure and peak-signalto-noise ratio are used to evaluate the performance of the proposed method. The experimental results show higher contrast 17.05% than the conventional method while a bit different in peak-signal-to-noise ratio.
  • Keywords
    cancer; image denoising; mammography; medical image processing; radiology; wavelet transforms; breast cancer; contrast enhancement; denoising; peak-signal-to-noise ratio; radiology; wavelet coefficients; wavelet transform; x-ray mammograms; Breast cancer; PSNR; Wavelet coefficients; Contrast Improvement; Denoising; Mammograms; Wavelet Transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering (JCSSE), 2013 10th International Joint Conference on
  • Conference_Location
    Maha Sarakham
  • Print_ISBN
    978-1-4799-0805-9
  • Type

    conf

  • DOI
    10.1109/JCSSE.2013.6567324
  • Filename
    6567324