Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 003-93386-5623
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2014.2305394
- Title of journal
- Ieee Transactions On Medical Imaging
- Article number
- -
- First page
- 1129
- Volume
- 33
- Issue
- 5
- ISSN
- 0278-0062
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
http://bura.brunel.ac.uk/handle/2438/10300
- 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
-
7
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 96
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in IEEE MI (10/102 Computer Science: Interdisciplinary Applications 2014), this paper is radically innovative in that it has developed an effective image methodology for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen. A novel switching-particle-swarm-optimization-based cellular neural network algorithm has been proposed for developing a robust method towards the accurate segmentation. The distinguishing novelties of this new developed algorithm have been acknowledged by the community.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
