A power differentiation method of fractal dimension estimation for 2-D signals

dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Νικήτα, Κωνσταντίνα Σ.	el
dc.date.accessioned	2015-01-13T11:40:37Z
dc.date.available	2015-01-13T11:40:37Z
dc.date.issued	2015-01-13
dc.identifier.uri	http://hdl.handle.net/11400/3921
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://store.elsevier.com/	en
dc.subject	Ultrasonic liver images
dc.subject	Power differentiation method
dc.subject	Υπέρηχες εικόνες ήπατος
dc.subject	Μέθοδος διαφοροποίησης ενέργειας
dc.title	A power differentiation method of fractal dimension estimation for 2-D signals	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Computer engineering
heal.classification	Ιατρική
heal.classification	Μηχανική υπολογιστών
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85029495
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Μηχανική υπολογιστών
heal.identifier.secondary	doi:10.1006/jvci.1998.0394
heal.language	en
heal.access	free
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	1998
heal.bibliographicCitation	Asvestas, P., Matsopoulos, G. and Nikita, K. (December 1998). A power differentiation method of fractal dimension estimation for 2-D signals. Journal of visual communication and image representation. 9(4). pp. 392–400. Available from: http://www.sciencedirect.com [Accessed 16/04/2002]	en
heal.abstract	Fractal dimension has been used for texture analysis as it is highly correlated with the human perception of surface roughness. Several methods have been proposed for the estimation of the fractal dimension of an image. One of the most popular is via its power spectrum density, provided that it is modeled as a fractional Brownian function. In this paper, a new method, called the power differentiation method (PDM), for estimating the fractal dimension of a two-variable signal from its power spectrum density is presented. The method is first applied to noise-free data of known fractal dimension. It is also tested with noise-corrupted and quantized data. Particularly, in the case of noise-corrupted data, the modified power differentiation method (MPDM) is developed, resulting in more accurate estimation of the fractal dimension. The results obtained by the PDM and the MPDM are compared directly to those obtained using four other well-known methods of fractal dimension. Finally, preliminary results for the classification of ultrasonic liver images, obtained by applying the new method, are presented.	en
heal.publisher	Academic Press	en
heal.journalName	Journal of visual communication and image representation	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true