NONLINEAR FILTERING OF MAGNETIC-RESONANCE TOMOGRAMS BY GEOMETRY-DRIVEN DIFFUSION

The paper deals with a nonuniform diffusion filtering of magnetic reso nance (MR) tomograms. Alternative digital schemes for discrete impleme ntation of the nonuniform diffusion equations are analyzed and tested. A novel locally adaptive conductance for the geometry-driven diffusio n (GDD) filtering is proposed. It is based on a measure of the neighbo rhood unhomogeneity adopted from the optimal orientation detection of linear symmetry. The algorithm performance is evaluated on the basis o f pseudoartificial 2D MR brain phantom and using the signal-to-noise r atio, as well as HC measure, developed for image discrimination charac terization. Three filtering methods are applied to MR images acquired by the fast 3D FLASH sequence. The results obtained are quantitatively and visually compared and discussed.