COMPRESSION

The size of medical images and signals is increasing rapidly with the improvement of acquisition technologies. As a consequence, transmission of these images and signals, as well as their storage is posing significant challenges for hospitals, clinics and other healthcare facilities. State-of-the art compression technologies pose viable solutions to these problems by substantially reducing the amount of bandwidth for transmission and memory for storage of such images and signals. Quantification of image degredation due to lossy compression is also an important topic in medical imaging, and several methods can be used.

• Wavelet and DCT Compression Technologies for Medical Images
  • JPEG, LJPEG, JPEG-LS, JPEG 2000 technologies
  • Lossy and lossless compression
  • Scalable compression
  • Automated ROI selection and encoding/decoding
  • Bit plane coding

• Lossy Compression Artifacts for Medical Images
  • Evaluation of compression ratios as a function of perceptual artifacts
  • Image degradation metrics - perceptual and nonperceptual
  • Identification of modalities more susceptible to particular artifacts

• Other Codecs for Medical Signals and Images
  • Arithmetic coding (including MQ-coding)
  • Huffman coding (static and adaptive codes)
  • Lemple-Ziv Welch (LZW)
  • Wavelet-based (and other transform-based) codecs
  • Prediction-based coders

Neuro CT and MRI compression. Left to right: Original CT (uncompressed), CT compressed with JPEG 2000 (15:1), original MRI, MRI compressed with JPEG 2000 (15:1).