An information preserving data compression for X-ray CT images using MMSE denoising

Kazuki Ogo, Motohiro Tabuchi, Nobumoto Yamane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image restoration methods based on a universal Gaussian mixture model (UNI-GMM) may realize minimum mean square error, under locally stationary assumption. Because the UNI-GMM appeared in the literatures observes the model in fixed size square blocks for simplicity, it has trade-off relation, i.e. large blocks become inconsistent to stationary assumption and small blocks diminish noise reduction performance. Arbitrary shaped observation block is known effective in this problem. In the case of UNI-GMM, multi-size observation block is under study to improve consistency of the locally stationary assumption. In this paper, this method is applied for information preserving X-ray CT image compression, in order to improve not only image quality but also compression rate in the diagnostic imaging systems, e.g. PACS system.

Original languageEnglish
Title of host publication2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Pages43-47
Number of pages5
DOIs
Publication statusPublished - Dec 1 2013
Event2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013 - Tokyo, Japan
Duration: Oct 1 2013Oct 4 2013

Publication series

Name2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013

Other

Other2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Country/TerritoryJapan
CityTokyo
Period10/1/1310/4/13

Keywords

  • X-ray CT
  • adaptive Wiener filter
  • entropy
  • lossless compression
  • universal Gaussian mixture distribution model

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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