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Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter

Abstract

A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors) 6 refs., 3 figs.2 tabs.
Authors:
Bajla, Ivan; [1]  Hollander, Igor [2] 
  1. Austrian Research Centres Sibersdorf, Department of High Performance Image Processing and Video-Technology, A-2444 Seibersdorf (Austria)
  2. Institute of information Processing, Austrian Academy of Sciences, Sonnenfelsgasse 19/2, 1010 Wien (Austria)
Publication Date:
Dec 31, 1998
Product Type:
Conference
Report Number:
INIS-CY-0004; CONF-9806202-
Reference Number:
SCA: 550602; PA: AIX-30:017092; EDB-99:043337; SN: 99002082135
Resource Relation:
Conference: 8. Mediterranean conference on medical and biological engineering and computing (Medicon `98), Lemesos (Cyprus), 14-17 Jun 1998; Other Information: DN: 6 refs., 3 figs.2 tabs. The proceedings of this meeting are available on CD-ROM in PDF, at the price of US$50, from the Cyprus Association of Medical Physics and Biomedical Engineering, P.O.Box 24039, 1700 Nicosia, Cyprus. The CD-ROM makes use of Abode Acrobat PDF (Portable Document Format). To read the files the Abode Acrobat reader has to be installed on your system. A copy is provided with the CD-ROM; PBD: 1998; Related Information: Is Part Of Proceedings of the 8. Mediterranean Conference on Medical and Biological Engineering and Computing (Medicon `98); Christofides, Stelios; Pattichis, Constantinos; Schizas, Christos; Keravnou-Papailiou, Elpida; Kaplanis, Prodromos; Spyros, Spyrou; Christodoulides, George; Theodoulou, Yiannis [eds.]; PB: 220 p.
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; BRAIN; DIAGNOSIS; NMR IMAGING; NUCLEAR MAGNETIC RESONANCE; PHANTOMS
OSTI ID:
331899
Research Organizations:
Cyprus Association of Medical Physics and Biomedical Engineering (CAMPBE), Nicosia (Cyprus); The Department of Computer Science, University of Cyprus, Nicosia (Cyprus)
Country of Origin:
Cyprus
Language:
English
Other Identifying Numbers:
Other: ON: DE99619912; ISBN 9963-607-14-4; TRN: CY9900015017092
Availability:
INIS; OSTI as DE99619912
Submitting Site:
INIS
Size:
pp. 118
Announcement Date:

Citation Formats

Bajla, Ivan, and Hollander, Igor. Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter. Cyprus: N. p., 1998. Web.
Bajla, Ivan, & Hollander, Igor. Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter. Cyprus.
Bajla, Ivan, and Hollander, Igor. 1998. "Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter." Cyprus.
@misc{etde_331899,
title = {Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter}
author = {Bajla, Ivan, and Hollander, Igor}
abstractNote = {A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors) 6 refs., 3 figs.2 tabs.}
place = {Cyprus}
year = {1998}
month = {Dec}
}