Computer aided diagnosis of CT focal liver lesions based on texture features, feature selection and ensembles of classifiers

Mougiakakou, SG; Valavanis, IK; Nikita, A; Nikita, KS

dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Valavanis, IK	en
dc.contributor.author	Nikita, A	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:23:43Z
dc.date.available	2014-03-01T01:23:43Z
dc.date.issued	2006	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17117
dc.subject	Computed Tomography	en
dc.subject	Computer Aided Diagnosis	en
dc.subject	Ensemble of Classifiers	en
dc.subject	Feature Selection	en
dc.subject	K Nearest Neighbor	en
dc.subject	Texture Features	en
dc.subject	Neural Network	en
dc.subject	Region of Interest	en
dc.title	Computer aided diagnosis of CT focal liver lesions based on texture features, feature selection and ensembles of classifiers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/0-387-34224-9_83	en
heal.identifier.secondary	http://dx.doi.org/10.1007/0-387-34224-9_83	en
heal.publicationDate	2006	en
heal.abstract	A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three k-nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes. © 2006 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/0-387-34224-9_83	en
dc.identifier.volume	204	en
dc.identifier.spage	705	en
dc.identifier.epage	712	en