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

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

Research output: Contribution to journalArticle

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 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
Volume9
Issue number1
DOIs
Publication statusPublished - 2019 Dec 1

Fingerprint

Costs and Cost Analysis
Datasets

ASJC Scopus subject areas

  • General

Cite this

Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage. / Dong, Pei; Cao, Xiaohuan; Yap, Pew Thian; Shen, Dinggang.

In: Scientific reports, Vol. 9, No. 1, 12703, 01.12.2019.

Research output: Contribution to journalArticle

@article{1b6fe1adda574616aab82cf037db89e8,
title = "Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage",
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 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.",
author = "Pei Dong and Xiaohuan Cao and Yap, {Pew Thian} and Dinggang Shen",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s41598-019-48491-9",
language = "English",
volume = "9",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage

AU - Dong, Pei

AU - Cao, Xiaohuan

AU - Yap, Pew Thian

AU - Shen, Dinggang

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85071747353&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071747353&partnerID=8YFLogxK

U2 - 10.1038/s41598-019-48491-9

DO - 10.1038/s41598-019-48491-9

M3 - Article

C2 - 31481695

AN - SCOPUS:85071747353

VL - 9

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 12703

ER -