Group-wise similarity registration of point sets using Student’s t-mixture model for statistical shape models
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-3956
- Type
- D - Journal article
- DOI
-
10.1016/j.media.2017.11.012
- Title of journal
- Medical Image Analysis
- Article number
- -
- First page
- 156
- Volume
- 44
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- 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
-
4
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a theoretical framework for statistical analysis of neural fiber tracts in the brain obtained from Magnetic Resonance Diffusion Tensor Imaging. The significance of the work derives from the rigorous proposed approach to spatially align (register) complex forms of data (comprising voxel-wise features such as positions, orientations of the fibers, etc.) acquired from multiple patients. It also derives the population-wise characteristics of the neural structure in various patient cohorts. The method has revealed significant differences between healthy, Mild Cognitive Impair, and Alzheimer’s Disease patients in terms of fractional anisotropy and orientation in various regions of the brain.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
