Statistical Shape Analysis: From Landmarks to Diffeomorphisms
Name
nihms-837301.pdf
Description
Accepted version
Size
620.62 KB
Format
Unknown
Checksum (MD5)
9155e702d9a184b4317a70d7b9bdb6cd
Author(s)
Zhang, Miaomiao
Golland, Polina
Date Issued
2016
Journal
Medical Image Analysis
Publisher
Elsevier BV
Citation
Zhang, M., and P. Golland. "Statistical Shape Analysis: From Landmarks to Diffeomorphisms." Med Image Anal 33 (2016): 155-8.
Version
Author's final manuscript
Abstract
© 2016 Elsevier B.V. We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.MEDIA.2016.06.025
