Effective sparse representation of X-Ray medical images


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.

Publication DOI: https://doi.org/10.1002/cnm.2886
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
Additional Information: 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.
Uncontrolled Keywords: greedy pursuit strategies,image approximation,sparse representations,Software,Modelling and Simulation,Biomedical Engineering,Molecular Biology,Computational Theory and Mathematics,Applied Mathematics
Publication ISSN: 2040-7947
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-12-04
Published Online Date: 2017-04-07
Accepted Date: 2017-03-31
Authors: Rebollo-Neira, Laura (ORCID Profile 0000-0002-7420-8977)



Version: Accepted Version

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