Linked Life Data

20418040

Source:http://linkedlifedata.com/resource/pubmed/id/20418040

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-5-21
pubmed:abstractText
Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1873-5894
pubmed:author
pubmed:copyrightInfo
Copyright 2010 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
721-38
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic.
pubmed:affiliation
Department of Electrical Engineering, National Central University, Jhongli, Taiwan, ROC.
pubmed:publicationType
Journal Article