A competitive scheme for storing sparse representation of X-Ray medical images
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 26058303
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0201455
- Title of journal
- PLoS ONE
- Article number
- e0201455
- First page
- -
- Volume
- 13
- Issue
- 8
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- 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
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Lessening the cardinality of medical data is crucial for remote diagnosis and treatments. Our approach renders high quality representation of typical X-Ray images for diagnosis, in terms of a significantly smaller number of variables in relation of the number of the intensity values producing the image. Moreover, in spite of the fact that it is not a technique designed for image compression, the reduced representation is encapsulated in a file significantly smaller than the commonly used JPEG format so in addition of reducing dimensionality for possible automatic analysis, the reduced representation can be transmitted faster than when in JPEG format.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
