Task-Based Evaulation of Image Filtering within a Class of Geometry-Driven-Diffusion Algorithms

A novel task-based algorithm performance evaluation technique is propose for the evaluation of geometry-driven diffusion (GDD) methods used for increasing the signal-to-noise ratio in MR tomograms. It is based on a probabilistic model of stepwise constant image corrupted the distribution parameters of the ran-dom variable derived from intensity gradient are used for characterization of staircase image artifacts in diffused images. The proposed evaluation tech-nique incorporates a "gold standard" of the GDD algorithms, defined as a dif-fusion process governed by ideal values of conductance