Application of region of interest definition to quadtree-based compression of ct images

Ethan J. Halpern, Ahalya Premkumar, David J. Mullen, Chris C. Ng, Howard M. Levy, Jeffrey H. Newhouse, E. Stephen Amis, Linda M. Sanders, In K. Mun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


A quadtree-based data compression algorithm can provide different levels of compression within and outside of regions of interest (ROIs). The current study shows whether ROI compression can provide greater compression or diagnostic accuracy than uniform quadtree compression. In 75 single CT images from 75 consecutive abdominal examinations, 43 abnormalities were identified and surrounded by ROIs. Three radiologists interpreted the images following (I) 50:1 compression of the entire image; (2) ROI compression at five decreasing compression ratios (with 50:1 compression outside the ROI); and (3) reversible (lossless) compression ratio 3:1) yielded a sensitivity of 96%. ROI compression of 15:1 was achieved with no loss of sensitivity; ROI compression of 28:1 yielded a sensitivity of 91% (not significantly different). At any given compression ratio, diagnostic sensitivity was greater with ROI compression than with uniform quadtree compression. For purposes of image archiving, quadtree-based ROI compression is superior to uniform compression of CT images.

Original languageEnglish (US)
Pages (from-to)703-707
Number of pages5
JournalInvestigative Radiology
Issue number6
StatePublished - Jun 1990
Externally publishedYes


  • Computer-assisted radiography
  • Digital radiography
  • Processing images
  • Storage and retrieval images

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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