Evaluation of texture analysis techniques for quantitative characterization of ultrasonic liver images

Pavlopoulos, S; Konnis, G; Kyriacou, E; Koutsouris, D; Zoumpoulis, P; Theotokas, I

dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Konnis, G	en
dc.contributor.author	Kyriacou, E	en
dc.contributor.author	Koutsouris, D	en
dc.contributor.author	Zoumpoulis, P	en
dc.contributor.author	Theotokas, I	en
dc.date.accessioned	2014-03-01T02:41:12Z
dc.date.available	2014-03-01T02:41:12Z
dc.date.issued	1996	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30421
dc.subject	Fractal Dimension	en
dc.subject	Texture Analysis	en
dc.subject	Tissue Characterization	en
dc.subject	Region of Interest	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Diseases	en
dc.subject.other	Fractals	en
dc.subject.other	Image analysis	en
dc.subject.other	Statistical methods	en
dc.subject.other	Tissue	en
dc.subject.other	Ultrasonic imaging	en
dc.subject.other	Cirrhosis	en
dc.subject.other	Fractal dimension texture analysis (FDTA)	en
dc.subject.other	Gray level difference statistics (GLDS)	en
dc.subject.other	Hepatoma	en
dc.subject.other	Liver	en
dc.subject.other	Texture analysis	en
dc.subject.other	Medical imaging	en
dc.title	Evaluation of texture analysis techniques for quantitative characterization of ultrasonic liver images	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.1996.652750	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.1996.652750	en
heal.publicationDate	1996	en
heal.abstract	In this study, we attempt to determine the efficacy of computer assisted ultrasonic liver tissue characterization using texture analysis techniques. Two different algorithms were used in this study; the gray level difference statistics (GLDS) and the fractal dimension texture analysis (FDTA). Both techniques were applied on three sets of ultrasonic liver images, normal-hepatoma-cirrhosis, all histologically proven. In all images, 32×32 pixel rectangular regions-of-interest were selected by specialized physicians and used in the analysis. FDTA was able to differentiate hepatoma from cirrhosis and normal liver with an accuracy of 90% and the GLDS was able to differentiate cirrhosis from normal with an accuracy of 75%. The combination of the two techniques proved to differentiate the three types with an overall accuracy of 81.7%.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.1996.652750	en
dc.identifier.volume	3	en
dc.identifier.spage	1151	en
dc.identifier.epage	1152	en