Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage

Authors
Dong, PeiCao, XiaohuanYap, Pew-ThianShen, Dinggang
Issue Date
3-9월-2019
Publisher
NATURE PUBLISHING GROUP
Citation
SCIENTIFIC REPORTS, v.9
Indexed
SCIE
SCOPUS
Journal Title
SCIENTIFIC REPORTS
Volume
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62921
DOI
10.1038/s41598-019-48491-9
ISSN
2045-2322
Abstract
Groupwise registration aligns a set of images to a common space. It can however be inefficient and ineffective when dealing with datasets with significant anatomical variations. To mitigate these problems, we propose a groupwise registration framework based on hierarchical multi-level and multi-resolution shrinkage of a graph set. First, to deal with datasets with complex in homogeneous image distributions, we divide the images hierarchically into multiple clusters. Since the images in each cluster have similar appearances, they can be registered effectively. Second, we employ a multi-resolution strategy to reduce computational cost. Experimental results on two public datasets show that our proposed method yields state-of-the-art registration accuracy with significantly reduced computational time.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE