Effective sparse representation of X-Ray medical images

Laura Rebollo-Neira*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Effective sparse representation of X-Ray medical images within the context of data reduction is considered. The proposed framework is shown to render an enormous reduction in the cardinality of the data set required to represent this class of images at very good quality. The goal is achieved by a) creating a dictionary of suitable elements for the image decomposition in the wavelet domain and b) applying effective greedy strategies for selecting the particular elements which enable the sparse decomposition of the wavelet coefficients.
The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements.
Original languageEnglish
Article numbere2886
JournalInternational Journal for Numerical Methods in Biomedical Engineering
Volume33
Issue number12
Early online date7 Apr 2017
DOIs
Publication statusPublished - 4 Dec 2017

Bibliographical note

This is the peer reviewed version of the following article: Rebollo-Neira, L. (2017). Effective sparse representation of X-Ray medical images. International Journal for Numerical Methods in Biomedical Engineering, in press, which has been published in final form at [Link to final article using the http://dx.doi.org/10.1002/cnm.2886. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Keywords

  • greedy pursuit strategies
  • image approximation
  • sparse representations

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