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

Pei Dong, Xiaohuan Cao, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 inhomogeneous 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.

Original languageEnglish
Article number12703
JournalScientific reports
Issue number1
Publication statusPublished - 2019 Dec 1

Bibliographical note

Funding Information:
This work was funded supported in part by NIH grants (AG053867 and EB008374).

Publisher Copyright:
© 2019, The Author(s).

ASJC Scopus subject areas

  • General


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