3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 973xq
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2014.03.078
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 16
- Volume
- 150
- Issue
- A
- ISSN
- 0925-2312
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2014
- 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
-
5
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper introduces a new approach to segment out the ventricular system in a series of high resolution T1-weighted Magnetic Resonance (MR) images from brain slices. The significance of this paper is that the proposed method accelerates the 3D surface reconstruction of the ventricles, is tolerant to noise and offers higher accuracy (e.g., eliminates outliers) when compared to the classical surface reconstruction and filtering processes such as the Voxel Grid. Methods like the proposed have the impact on improving population health, since automatic segmentation and reconstruction of organs is a challenging task in computer aided diagnosis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
