Fuzzy neural network-based texture analysis of ultrasonic images

Pavlopoulos, S; Kyriacou, E; Koutsouris, D; Blekas, K; Stafylopatis, A; Zoumpoulis, P

dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Kyriacou, E	en
dc.contributor.author	Koutsouris, D	en
dc.contributor.author	Blekas, K	en
dc.contributor.author	Stafylopatis, A	en
dc.contributor.author	Zoumpoulis, P	en
dc.date.accessioned	2014-03-01T01:49:24Z
dc.date.available	2014-03-01T01:49:24Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25765
dc.subject	Fractal Dimension	en
dc.subject	Fuzzy Clustering	en
dc.subject	Fuzzy Neural Network	en
dc.subject	Fuzzy Set	en
dc.subject	Image Texture Analysis	en
dc.subject	Neural Network Classifier	en
dc.subject	Texture Analysis	en
dc.subject	First Order	en
dc.subject	Grade of Membership	en
dc.subject	voronoi diagram	en
dc.title	Fuzzy neural network-based texture analysis of ultrasonic images	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/51.816243	en
heal.identifier.secondary	http://dx.doi.org/10.1109/51.816243	en
heal.publicationDate	2000	en
heal.abstract	The efficacy of a novel fuzzy neural network classifier for the characterization of ultrasonic liver images based on texture analysis techniques is investigated. Classification features are extracted with the use of image texture analysis techniques such as fractal dimension texture analysis, spatial gray-level dependence matrices, gray-level difference statistics, gray-level run-length statistics, and first-order gray-level parameters. These features are fed to	en
heal.journalName	IEEE Pulse	en
dc.identifier.doi	10.1109/51.816243	en