|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:issue |
2
|
|
pubmed:dateCreated |
2008-4-16
|
|
pubmed:abstractText |
We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.
|
|
pubmed:grant |
http://linkedlifedata.com/resource/pubmed/grant/P30 HD018655,
http://linkedlifedata.com/resource/pubmed/grant/P30 HD018655-26,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR013218,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR013218-010001,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR013218-010002,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR013218-010010,
http://linkedlifedata.com/resource/pubmed/grant/R01 RR021885,
http://linkedlifedata.com/resource/pubmed/grant/R01 RR021885-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R01 RR021885-02,
http://linkedlifedata.com/resource/pubmed/grant/R03 CA126466,
http://linkedlifedata.com/resource/pubmed/grant/R03 CA126466-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R03 CA126466-02,
http://linkedlifedata.com/resource/pubmed/grant/R21 MH067054,
http://linkedlifedata.com/resource/pubmed/grant/R21 MH067054-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R21 MH067054-02,
http://linkedlifedata.com/resource/pubmed/grant/U41 RR019703,
http://linkedlifedata.com/resource/pubmed/grant/U54 EB005149
|
|
pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-10331689,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-11025519,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-12023417,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-14683705,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-15037456,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-15325368,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-15814922,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-15917106,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-15978841,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16385020,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16526017,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16624579,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16685845,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16905335,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18180197-16926104
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
|
pubmed:month |
Apr
|
|
pubmed:issn |
1361-8423
|
|
pubmed:author |
|
|
pubmed:issnType |
Electronic
|
|
pubmed:volume |
12
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
191-202
|
|
pubmed:dateRevised |
2011-9-26
|
|
pubmed:meshHeading |
pubmed-meshheading:18180197-Algorithms,
pubmed-meshheading:18180197-Artificial Intelligence,
pubmed-meshheading:18180197-Brain,
pubmed-meshheading:18180197-Cluster Analysis,
pubmed-meshheading:18180197-Diffusion Magnetic Resonance Imaging,
pubmed-meshheading:18180197-Humans,
pubmed-meshheading:18180197-Image Enhancement,
pubmed-meshheading:18180197-Image Interpretation, Computer-Assisted,
pubmed-meshheading:18180197-Imaging, Three-Dimensional,
pubmed-meshheading:18180197-Likelihood Functions,
pubmed-meshheading:18180197-Models, Biological,
pubmed-meshheading:18180197-Models, Statistical,
pubmed-meshheading:18180197-Nerve Fibers, Myelinated,
pubmed-meshheading:18180197-Pattern Recognition, Automated,
pubmed-meshheading:18180197-Reproducibility of Results,
pubmed-meshheading:18180197-Sensitivity and Specificity
|
|
pubmed:year |
2008
|
|
pubmed:articleTitle |
A unified framework for clustering and quantitative analysis of white matter fiber tracts.
|
|
pubmed:affiliation |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, USA. mmaddah@mit.edu
|
|
pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
|
