Locally adaptive conductance in geometry-driven-diffusion filtering of magnetic resonance tomograms

作     者：Bajla, I Holländer, I 

作者机构：Austrian Res Ctr Seibersdorf Dept High Performance Image Proc & Video Technol A-2444 Seibersdorf Austria Univ Manchester Neurosci & Psychiat Unit Manchester M13 9PT Lancs England 

出 版 物：《IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING》 (IEE Proc Vision Image Signal Proc)

年 卷 期：2000年第147卷第3期

页      面：271-282页

核心收录：

主　　题：.interiors conductance appro lias adaptivc tlic llie GDD calculatcd iniagc MI brain Llic considcr liavc 

摘      要：A novel methodology for locally adapting the exponential conductance in geometry-driven diffusion (GDD) is proposed which employs pixel dissimilarity measures. Two alternative approaches are developed;both are based on a transient interval, within which the relaxation parameter IC is selected. In the first case: the limits of the interval are derived from global quantiles of the intensity gradients: in the second case, they are derived from the optimal variable parameter K-opt, calculated from a specific cost function This function is designed using intensity gradient histograms of region interiors and boundaries in an appropriate image template of an MR brain tomogram. As a local measure, the mean direction dissimilarity has been used. Computer experiments with the locally adaptive geometry-driven diffusion filtering of an MR-head phantom have been performed and quantitatively evaluated. They include, as a reference, two other GDD filtering methods.