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MR image compression using iterated function systems

D C Popescu1, H Yan

  • 1Department of Electrical Engineering, University of Sydney, NSW, Australia.

Magnetic Resonance Imaging
|January 1, 1993
PubMed
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This study introduces a novel method for Magnetic Resonance (MR) image compression using fractal models. The technique efficiently encodes MR images by exploiting self-similarities for data reduction.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Image Processing

Background:

  • Magnetic Resonance (MR) imaging generates large datasets, necessitating efficient compression techniques.
  • Existing compression methods may not fully exploit the inherent self-similarities within MR images.
  • Fractal-based approaches offer potential for high compression ratios by modeling image structures.

Purpose of the Study:

  • To develop and evaluate a novel MR image compression method using iterated function systems (IFS) and fractal models.
  • To leverage image self-similarities for effective data reduction in MR imaging.
  • To introduce a fast matching procedure for efficient encoding of MR images.

Main Methods:

  • Utilizing iterated function systems (IFS) based on fractal models for image representation.

Related Experiment Videos

  • Exploiting image self-similarities by matching similar image blocks.
  • Implementing a fast matching procedure for efficient image encoding.

    • Main Results:

    • The proposed method achieves compression by effectively matching similar image blocks.
    • The fractal model-based approach demonstrates efficiency in encoding MR images.
    • Successful testing of the compression method on real-world MR image datasets.

    Conclusions:

    • Iterated function systems based on fractal models provide an effective approach for MR image compression.
    • The proposed fast matching procedure enhances the efficiency of MR image encoding.
    • The method shows promise for practical application in medical imaging data management.