Evaluation of Texture Features in Hepatic Tissue Characterization from Non-enhanced CT Images

Nikita, A; Nikita, K; Mougiakakou, S; Valavanis, I

dc.contributor.author	Nikita, A	en
dc.contributor.author	Nikita, K	en
dc.contributor.author	Mougiakakou, S	en
dc.contributor.author	Valavanis, I	en
dc.date.accessioned	2014-03-01T02:50:57Z
dc.date.available	2014-03-01T02:50:57Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35246
dc.subject	Computed Tomography	en
dc.subject	Feature Selection	en
dc.subject	Feed Forward Neural Network	en
dc.subject	Fractal Dimension	en
dc.subject	Genetic Algorithm	en
dc.subject	Receiver Operator Characteristic	en
dc.subject	Roc Curve	en
dc.subject	Texture Features	en
dc.subject	Tissue Characterization	en
dc.subject	First Order	en
dc.subject	Hepatocellular Carcinoma	en
dc.subject	Region of Interest	en
dc.title	Evaluation of Texture Features in Hepatic Tissue Characterization from Non-enhanced CT Images	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2007.4353145	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2007.4353145	en
heal.publicationDate	2007	en
heal.abstract	Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced computed tomography (CT) images. Regions of interest (rois) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en
dc.identifier.doi	10.1109/IEMBS.2007.4353145	en