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Lossless medical image compression by multilevel decomposition

K S Kang1, H W Park

  • 1Department of Information and Communication Engineering, Korea Advanced Institute of Science and Technology, Seoul.

Journal of Digital Imaging
|February 1, 1996
PubMed
Summary
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This study introduces a novel lossless medical image compression algorithm. It effectively reduces data size while preserving diagnostic quality for applications like CT and MRI scans.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Signal Processing

Background:

  • Lossless image compression is crucial in medical imaging to prevent diagnostic errors.
  • High spatial correlation in medical images presents an opportunity for efficient compression.

Purpose of the Study:

  • To propose and evaluate a novel lossless compression algorithm for medical images.
  • To leverage multi-level decomposition, prediction, and classification for enhanced compression.

Main Methods:

  • The algorithm employs a multi-level decomposition scheme using subsampling to divide images into subimages.
  • One subimage serves as a reference for predicting the others, with prediction errors classified based on reference characteristics.
  • Classified prediction errors are entropy-coded, with the process iterated on reference subimages.

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Main Results:

  • The algorithm was tested on chest radiographs, computed tomography (CT), and magnetic resonance (MR) images.
  • Performance was benchmarked against established lossless compression techniques.

Conclusions:

  • The proposed algorithm demonstrates potential for effective lossless compression of medical images.
  • It offers a viable solution for preserving diagnostic integrity in medical image archiving and transmission.