Linked Life Data

17945890

Source:http://linkedlifedata.com/resource/pubmed/id/17945890

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-10-23
pubmed:abstractText
In this paper, we propose a knowledge-driven highly automatic methodology for extracting the masseter from magnetic resonance (MR) data sets for clinical purposes. The masseter is a muscle of mastication which acts to raise the jaw and clench the teeth. In our initial work, we designed a process which allowed us to perform 2-D segmentation of the masseter on 2-D MR images. In the methodology proposed here, we make use of ground truth to first determine the index of the MR slice in which we will carry out 2-D segmentation of the masseter. Having obtained the 2-D segmentation, we will make use of it to determine the region of interest (ROI) of the masseter in the other MR slices belonging to the same data set. The upper and lower thresholds applied to these MR slices, for extraction of the masseter, are determined through the histogram of the 2-D segmented masseter. Visualization of the 3-D masseter is achieved via volume rendering. Our methodology has been applied to five MR data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual contour tracings, obtaining an average accuracy of 83.5%
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5294-7
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Knowledge-driven 3-D extraction of the masseter from MR data.
pubmed:affiliation
NUS Graduate School for Integrative Sciences & Engineering, Singapore.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't