Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

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

dc.contributor.author	Valavanis, IK	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Nikita, A	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:44:37Z
dc.date.available	2014-03-01T02:44:37Z
dc.date.issued	2007	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31904
dc.subject.other	Computerized tomography	en
dc.subject.other	Fractal dimension	en
dc.subject.other	Image enhancement	en
dc.subject.other	Neural networks	en
dc.subject.other	Radiology	en
dc.subject.other	Statistical mechanics	en
dc.subject.other	Hepatic tissue characterization	en
dc.subject.other	Radiologists	en
dc.subject.other	Spatial Gray Level Dependence Matrix (SGLDM)	en
dc.subject.other	Texture features	en
dc.subject.other	Tissue engineering	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.identifier.secondary	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, Ave distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or Its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802±0.083) In the discrimination of hepatic tissue. © 2007 IEEE.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2007.4353145	en
dc.identifier.volume	2007	en
dc.identifier.spage	3741	en
dc.identifier.epage	3744	en