Linked Life Data

18215504

Source:http://linkedlifedata.com/resource/pubmed/id/18215504

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-2-25
pubmed:abstractText
Mutual information (MI)-based image registration has been proved to be very effective in multimodal medical image applications. For computing the mutual information between two images, the joint histogram needs to be estimated. As we know, the joint histogram estimation through linear interpolation and partial volume (PV) interpolation methods may result in the emergency of the local extreme in mutual information registration function. The local extreme is likely to hamper the optimization process and influence the registration accuracy. In this paper, we present a novel joint histogram estimation method (HPV) by using an approximate function of Hanning windowed sinc as kernel function of partial volume interpolation. We apply it to both rigid registration and non-rigid registration. In addition, we give a new method estimating the gradient of mutual information with respect to the model parameters during non-rigid registration. By the experiments on both synthetic and real images, it is clearly shown that the new algorithm has the ability to reduce the local extreme, and the registration accuracy is improved.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0895-6111
pubmed:author
pubmed:issnType
Print
pubmed:volume
32
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
202-9
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Mutual information-based multimodal image registration using a novel joint histogram estimation.
pubmed:affiliation
Department of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Min Hang, Shanghai 200240, PR China. luxsyyl@sjtu.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't