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.
The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements.
| Original language | English |
|---|---|
| Article number | e2886 |
| Journal | International Journal for Numerical Methods in Biomedical Engineering |
| Volume | 33 |
| Issue number | 12 |
| Early online date | 7 Apr 2017 |
| DOIs | |
| Publication status | Published - 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