11797941

Source:http://linkedlifedata.com/resource/pubmed/id/11797941

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0011905, umls-concept:C0036576, umls-concept:C0185125, umls-concept:C0243161, umls-concept:C1533716, umls-concept:C1709100, umls-concept:C2348519
pubmed:issue	12
pubmed:dateCreated	2002-1-18
pubmed:abstractText	The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. Consequently, MI can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables. This study was based on a database of statistical texture features extracted from perfusion lung scans. The ultimate goal was to select the optimal subset of features for the computer-aided diagnosis of acute pulmonary embolism (PE). Initially, the study addressed issues regarding the approximation of MI in a limited dataset as it is often the case in CAD applications. The MI selected features were compared to those features selected using stepwise linear discriminant analysis and genetic algorithms for the same PE database. Linear and nonlinear decision models were implemented to merge the selected features into a final diagnosis. Results showed that the MI is an effective feature selection criterion for nonlinear CAD models overcoming some of the well-known limitations and computational complexities of other popular feature selection techniques in the field.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0425746
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Dec
pubmed:issn	0094-2405
pubmed:author	pubmed-author:FloydC ECEJr, pubmed-author:FrederickE DED, pubmed-author:MarrasT KTK, pubmed-author:TourassiG DGD
pubmed:issnType	Print
pubmed:volume	28
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	2394-402
pubmed:dateRevised	2006-11-15
pubmed:meshHeading	pubmed-meshheading:11797941-Diagnosis, Computer-Assisted, pubmed-meshheading:11797941-Humans, pubmed-meshheading:11797941-Models, Statistical, pubmed-meshheading:11797941-Normal Distribution, pubmed-meshheading:11797941-Software
pubmed:year	2001
pubmed:articleTitle	Application of the mutual information criterion for feature selection in computer-aided diagnosis.
pubmed:affiliation	Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. gt@deckard.mc.duke.edu
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't