A fast global variational bias field correction method for MR images

Magnetic resonance (MR) images are prone to inhomogeneity artefacts that hinder an efficient automatic segmentation. Existing correction methods are often dependent on initialization and computationally expensive. This paper proposes a novel variational approach for the simultaneous bias field correction and image segmentation together with its efficient implementation, which produces the global solution that does not depend on initializations. The method is compared against another recently proposed method in terms of speed, efficiency, and performance.