Linked Life Data

17354854

Source:http://linkedlifedata.com/resource/pubmed/id/17354854

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2007-3-14
pubmed:abstractText
In this paper, an automatic knowledge-based framework for level set segmentation of 3D calvarial tumors from Computed Tomography images is presented. Calvarial tumors can be located in both soft and bone tissue, occupying wide range of image intensities, making automatic segmentation and computational modeling a challenging task. The objective of this study is to analyze and validate different approaches in intensity priors modeling with an attention to multiclass problems. One, two, and three class Gaussian mixture models and a discrete model are evaluated considering probability density modeling accuracy and segmentation outcome. Segmentation results were validated in comparison to manually segmented golden standards, using analysis in ROC (Receiver Operating Curve) space and Dice similarity coefficient.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
864-71
pubmed:dateRevised
2009-12-11
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Modeling of intensity priors for knowledge-based level set algorithm in calvarial tumors segmentation.
pubmed:affiliation
Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Germany. popovic@hia.rwth-aachen.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies