Wavelet-based medical image compression with adaptive prediction

Yao Tien Chen, Din Chang Tseng

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

A lossless wavelet-based image compression method with adaptive prediction is proposed. Firstly, we analyze the correlations between wavelet coefficients to identify a proper wavelet basis function, then predictor variables are statistically test to determine which relative wavelet coefficients should be included in the prediction model. At last, prediction differences are encoded by an adaptive arithmetic encoder. Instead of relying on a fixed number of predictors on fixed locations, we proposed the adaptive prediction approach to overcome the multicollinearity problem. The proposed innovative approach integrating correlation analysis for selecting wavelet basis function with predictor variable selection is fully achieving high accuracy of prediction. Experimental results show that the proposed approach indeed achieves a higher compression rate on CT, MRI and ultrasound images comparing with several state-of-the-art methods.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalComputerized Medical Imaging and Graphics
Volume31
Issue number1
DOIs
StatePublished - Jan 2007

Keywords

  • Adaptive arithmetic coding
  • Image compression
  • Medical image
  • Multicollinearity problem
  • Selection of predictor variables

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