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

Asvestas, P; Matsopoulos, GK; Nikita, KS

dc.contributor.author	Asvestas, P	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:13:32Z
dc.date.available	2014-03-01T01:13:32Z
dc.date.issued	1998	en
dc.identifier.issn	1047-3203	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12548
dc.subject	Fractal Dimension	en
dc.subject	Human Perception	en
dc.subject	Surface Roughness	en
dc.subject	Texture Analysis	en
dc.subject	Power Spectrum Density	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Brownian movement	en
dc.subject.other	Fractals	en
dc.subject.other	Image quality	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optical correlation	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Fractal dimension estimation	en
dc.subject.other	Modified power differentiation methods (MPDM)	en
dc.subject.other	Image analysis	en
dc.title	A power differentiation method of fractal dimension estimation for 2-D signals	en
heal.type	journalArticle	en
heal.identifier.primary	10.1006/jvci.1998.0394	en
heal.identifier.secondary	http://dx.doi.org/10.1006/jvci.1998.0394	en
heal.language	English	en
heal.publicationDate	1998	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. (C) 1998 Academic Press.	en
heal.publisher	ACADEMIC PRESS INC	en
heal.journalName	Journal of Visual Communication and Image Representation	en
dc.identifier.doi	10.1006/jvci.1998.0394	en
dc.identifier.isi	ISI:000077998000011	en
dc.identifier.volume	9	en
dc.identifier.issue	4	en
dc.identifier.spage	392	en
dc.identifier.epage	400	en