A novel methodology for locally adapting the exponential conductance in geometry-drivendiffusion (GDD) is proposed which employs pixel dissimilarity measures. Two alternative approaches are developed;both are based o...
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A novel methodology for locally adapting the exponential conductance in geometry-drivendiffusion (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-drivendiffusionfiltering of an MR-head phantom have been performed and quantitatively evaluated. They include, as a reference, two other GDD filtering methods.
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