A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets

  • Authors:

  • Xiaojun Qi;John M. Tyler

  • Affiliations:

  • Department of Computer Science, Utah State University, 4205 Old Main Hill, Logan UT 84322, USA;Department of Computer Science, Louisiana State University, 298 Coates Hall Baton Rouge, LA 70803, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2005

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Abstract

This paper presents a novel and efficient diagnostically lossless compression for 3D medical image sets. This compression scheme provides the 3D medical image sets with a progressive transmission capability. An automated filter-and-threshold based preprocessing technique is used to remove noise outside the diagnostic region. Then a wavelet decomposition feature vector based approach is applied to determine the reference image for the entire 3D medical image set. The selected reference image contains the most discernible anatomical structures within a relative large diagnostic region. It is progressively encoded by a lossless embedded zerotree wavelet method so the validity of an entire set can be determined early. This preprocessing technique is followed by an optimal predictor plus a 1st-level integer wavelet transform to de-correlate the 3D medical image set. Run-length and arithmetic coding are used to further remove coding redundancy. This diagnostically lossless compression method achieves an average compression of 2.1038, 2.4292, and 1.6826 bits per pixel for three types of 3D magnetic resonance image sets. The integrated progressive transmission capability degrades the compression performance by an average of 7.25%, 6.60%, and 4.49% for the above three types. Moreover, our compression without and with progressive transmission achieves better compression than the state-of-the-art.