Discrete wavelet transform vs. wavelet packets for texture analysis of ultrasound images of carotid atherosclerosis

Tsiaparas, N; Golemati, S; Stoitsis, J; Nikita, KS

dc.contributor.author	Tsiaparas, N	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Stoitsis, J	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:46:07Z
dc.date.available	2014-03-01T02:46:07Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32553
dc.subject	Carotid atherosclerosis	en
dc.subject	Haar	en
dc.subject	Texture analysis	en
dc.subject	Ultrasound imaging	en
dc.subject	Wavelet analysis	en
dc.subject	Wavelet packets	en
dc.subject.other	Atherosclerotic plaque	en
dc.subject.other	Basis functions	en
dc.subject.other	Decomposition scheme	en
dc.subject.other	Descriptors	en
dc.subject.other	Haar	en
dc.subject.other	Haar filter	en
dc.subject.other	Low frequency	en
dc.subject.other	Subimages	en
dc.subject.other	Texture analysis	en
dc.subject.other	Ultrasound images	en
dc.subject.other	Ultrasound imaging	en
dc.subject.other	Wavelet Packet	en
dc.subject.other	Wavelet packets	en
dc.subject.other	Discrete wavelet transforms	en
dc.subject.other	Image processing	en
dc.subject.other	Information technology	en
dc.subject.other	Textures	en
dc.subject.other	Ultrasonics	en
dc.subject.other	Wavelet analysis	en
dc.subject.other	Wavelet decomposition	en
dc.title	Discrete wavelet transform vs. wavelet packets for texture analysis of ultrasound images of carotid atherosclerosis	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITAB.2009.5394445	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITAB.2009.5394445	en
heal.identifier.secondary	5394445	en
heal.publicationDate	2009	en
heal.abstract	In this paper, a scale/frequency approach, based on the wavelet transform, was used in an attempt to characterize carotid atherosclerotic plaque from B-mode ultrasound. Two wavelet decomposition schemes, namely the discrete wavelet transform (DWT) and wavelet packets (WP), and three basis functions, namely Haar, symlet3 and biorthogonal3.1, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. A total of 12 detail sub-images were extracted using the DWT and 255 using the WP decomposition schemes. It was shown that WP analysis by the use of Haar filter and the l-1 norm as texture descriptor could reveal differences not only in high but also in low frequencies, and therefore characterize efficiently the atheromatous tissue. Additional studies applying and further extending the above methodology are required to ensure the usefulness of wavelet-based texture analysis of carotid atherosclerosis. ©2009 IEEE.	en
heal.journalName	Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009	en
dc.identifier.doi	10.1109/ITAB.2009.5394445	en