Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images

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

dc.contributor.author	Kyriacou, E	en
dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Konnis, G	en
dc.contributor.author	Koutsouris, D	en
dc.contributor.author	Zoumpoulis, P	en
dc.contributor.author	Theotokas, I	en
dc.date.accessioned	2014-03-01T02:41:19Z
dc.date.available	2014-03-01T02:41:19Z
dc.date.issued	1997	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30458
dc.subject	Fractal Dimension	en
dc.subject	Image Texture Analysis	en
dc.subject	K Nearest Neighbor	en
dc.subject	Liver Disease	en
dc.subject	Texture Analysis	en
dc.subject	Texture Features	en
dc.subject	Tissue Characterization	en
dc.subject	Ultrasound	en
dc.subject	First Order	en
dc.subject	Region of Interest	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Image analysis	en
dc.subject.other	Statistical mechanics	en
dc.subject.other	Tissue	en
dc.subject.other	Ultrasonic imaging	en
dc.subject.other	Diffused liver disease	en
dc.subject.other	Image texture analysis	en
dc.subject.other	Ultrasound B scan images	en
dc.subject.other	Medical imaging	en
dc.title	Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NSSMIC.1997.670599	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NSSMIC.1997.670599	en
heal.publicationDate	1997	en
heal.abstract	In this study we evaluate the accuracy of image texture analysis techniques in the characterization of ultrasonic liver images. The texture analysis techniques used are the Fractal Dimension Texture Analysis (FDTA), the Spatial Gray Level Dependence Matrices (SGLDM), the Gray Level Difference Statistics (GLDS), the Gray Level Run Length Statistics (RUNL), and First Order gray level Parameters (FOP). The algorithms were applied on three sets of u/s liver images, fatty, cirrhosis, normal, (30 samples each) all histologically proven. Textural features were extracted using regions of interest (ROI) of 32×32 pixels in size. Results were classified using the K-Nearest Neighbor (K-NN) classifier. The FDTA and SGLDM were able to characterize the three sets with accuracy of 80%, GLDS achieved 78.9% while RUNL and FOP both achieved 77.8% accuracy. Combination of GLDS and RUNL gave 81.1%, while combination of FDTA and SGLDM gave 82.2% accuracy.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	IEEE Nuclear Science Symposium & Medical Imaging Conference	en
dc.identifier.doi	10.1109/NSSMIC.1997.670599	en
dc.identifier.volume	2	en
dc.identifier.spage	1479	en
dc.identifier.epage	1483	en