Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks
- Submitting institution
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University of Dundee
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 28402757
- Type
- D - Journal article
- DOI
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10.1109/TMI.2016.2570123
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 2381
- Volume
- 35
- Issue
- 11
- ISSN
- 0278-0062
- Open access status
- Not compliant
- Month of publication
- May
- Year of publication
- 2016
- 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
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1
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The SCIRD-TS filters proposed and optimized in this paper have been implemented in VAMPIRE, a software package distributed to clinical and computational groups around the world (recently Prof Giancardo, University of Texas; Prof Chiquet, University of Grenoble; Prof Frangi, University of Leeds; Dr Keane, Moorfields/UCL; Dr Mohan’s Diabetes Speciality Hospitals, India) researching retinal vascular biomarkers for dementias, cardiovascular conditions, and diabetes complications. SCIRD-TS is also used for vessel segmentation in OCT angiography images of the retina to study patient stratification by microvasculature fractal dimension, one of the most promising retinal features (Paterson et al., Investigative Opthalmology & Visual Science, 2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -