A novel approach to medical image compression

  • Authors:

  • Matthew J. Zukoski;Terrance Boult;Tunc Iyriboz

  • Affiliations:

  • Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA 18766, USA.;Department of Computer Science, University of Colorado at Colorado Springs, Colorado Springs, CO 80918, USA.;College of Medicine, Penn State University, Hershey, PA 17033, USA

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2006

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Abstract

As medical/biological imaging facilities move towards complete film-less imaging, compression plays a key role. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximise compression while maintaining clinical relevance and balancing legal risk. This paper proposes a novel model-based compression technique that makes use of clinically relevant regions as defined by radiologists. Lossless compression is used in these clinically relevant regions, and lossy compression is used everywhere else.