Medical Image Compression Based on Region of Interest, With Application to Colon CT Images

Abstract

CT or MRI Medical imaging produce human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image qualify only in the region of interest, i.e., in diagnostically important regions. This paper discusses a hybrid model of lossless compression in the region of interest, with high-rate, motion-compensated, lossy compression in other regions. We evaluate our method on medical CT images, and show that it outperforms other common compression schemes, such as discrete cosine transform, vector quantization, and principal component analysis. In our experiments, we emphasize CT images of the human colon.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411022

Entities

People

  • Bernd Girod
  • Carlo Tomasi
  • Chris Beaulieu
  • Salih B. Gokturk

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Coders
  • Coding
  • Colon
  • Colon Cancer
  • Compression
  • Computer Programming
  • Computer Science
  • Data Sets
  • Detection
  • Electrical Engineering
  • Engineering
  • Factor Analysis
  • Image Processing
  • Imaging Techniques
  • Three Dimensional
  • X-Ray Computed Tomography

Fields of Study

  • Computer science

Readers

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