An Implementation of Independent Component Analysis for 3D Statistical Shape Analysis
- Submitting institution
-
University of Durham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 103291
- Type
- D - Journal article
- DOI
-
10.1016/j.bspc.2014.06.003
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- -
- First page
- 345
- Volume
- 13
- Issue
- -
- ISSN
- 17468094
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
https://doi.org/10.1016/j.bspc.2014.06.003
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper provides a unique presentation of independent component analysis, particularly in the use of the continuous form for this signal processing approach. It has thus become recognised as a fundamental reference for using independent component analysis for three-dimensional statistical shape analysis, especially for biomedical applications. This approach provides the capability to extract features from medical imaging data that are significantly different than the traditional processing methods and potentially uniquely valuable (e.g., for disease diagnosis). As such, this seminal work has been a common reference for an approach that should be considered for any such application of statistical shape analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
