Medical image compression and analysis using wavelet modulus maxima decomposition

  • Authors:

  • Jan T. Bialasiewicz;Lisa S. Redmond

  • Affiliations:

  • University of Colorado at Denver and Health Sciences Center, Department of Electrical Engineering, Denver, Colorado;University of Colorado at Denver and Health Sciences Center, Department of Electrical Engineering, Denver, Colorado

  • Venue:
  • Machine Graphics & Vision International Journal
  • Year:
  • 2007

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Abstract

A method of highly effective biomedical image compression that includes the reconstruction process with a good convergence rate is presented in the paper. It represents an image in the form of its wavelet modulus maxima decomposition. The technique allows the compressed image representation to include only those wavelet transform coefficients that correspond to the wavelet transform modulus maxima that are determined for each resolution level. The proposed approach to analysis of medical images uses the wavelet modulus maxima decomposition to enhance image features that are not visually apparent. The transient behavior of pixel intensities (that corresponds to edges and singular points) is used for image enhancement. The detection of edges is realized by detecting modulus maxima in a two-dimensional dyadic wavelet transform at the proper scale. This approach to image analysis aims at determining structures of the diseased tissue that are represented by the image edges. It is expected that this technique will help with early detection of cancer when routine interpretation of CT scans is inconclusive and biopsy would be required.