Linked Life Data

20580597

Source:http://linkedlifedata.com/resource/pubmed/id/20580597

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-7-14
pubmed:abstractText
Medical image registration is a challenging problem, especially when there is large anatomical variation in the anatomies. Geodesic registration methods have been proposed to solve the large deformation registration problem. However, analytically defined geodesic paths may not coincide with biologically plausible paths of registration, since the manifold of diffeomorphisms is immensely broader than the manifold spanned by diffeomorphisms between real anatomies. In this paper, we propose a novel framework for large deformation registration using the learned manifold of anatomical variation in the data. In this framework, a large deformation between two images is decomposed into a series of small deformations along the shortest path on an empirical manifold that represents anatomical variation. Using a manifold learning technique, the major variation of the data can be visualized by a low-dimensional embedding, and the optimal group template is chosen as the geodesic mean on the manifold. We demonstrate the advantages of the proposed framework over direct registration with both simulated and real databases of brain images.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1361-8423
pubmed:author
pubmed:copyrightInfo
Copyright 2010 Elsevier B.V. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
633-42
pubmed:dateRevised
2011-10-3
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
GRAM: A framework for geodesic registration on anatomical manifolds.
pubmed:affiliation
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA. jihun.hamm@uphs.upenn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural