Effective sparse representation of X-Ray medical images
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21886372
- Type
- D - Journal article
- DOI
-
10.1002/cnm.2886
- Title of journal
- International Journal for Numerical Methods in Biomedical Engineering
- Article number
- e2886
- First page
- -
- Volume
- 33
- Issue
- 12
- ISSN
- 2040-7947
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a technical paper which challenges the established paradigm for transformation of X-ray medical images. Proof of concepts are presented demonstrating that, in spite of being adaptive, the implementation of the described techniques is fast enough to be of practical relevance even when implemented in a small laptop with single processor. The software consisting of a library of computational routines for facilitating the implementation of the methods has been made available on a dedicated website for free download: http://www.nonlinear-approx.info/examples/node06.html
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
