Linked Life Data

22003706

Source:http://linkedlifedata.com/resource/pubmed/id/22003706

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 3
pubmed:dateCreated
2011-10-18
pubmed:abstractText
This paper presents a new bronchoscope motion tracking method that utilizes manifold modeling and sequential Monte Carlo (SMC) sampler to boost navigated bronchoscopy. Our strategy to estimate the bronchoscope motions comprises two main stages: (1) bronchoscopic scene identification and (2) SMC sampling. We extend a spatial local and global regressive mapping (LGRM) method to Spatial-LGRM to learn bronchoscopic video sequences and construct their manifolds. By these manifolds, we can classify bronchoscopic scenes to bronchial branches where a bronchoscope is located. Next, we employ a SMC sampler based on a selective image similarity measure to integrate estimates of stage (1) to refine positions and orientations of a bronchoscope. Our proposed method was validated on patient datasets. Experimental results demonstrate the effectiveness and robustness of our method for bronchoscopic navigation without an additional position sensor.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
248-55
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
ManiSMC: a new method using manifold modeling and sequential Monte Carlo sampler for boosting navigated bronchoscopy.
pubmed:affiliation
Graduate School of Information Science, Nagoya University, Japan.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't