Rough Set Based Homogeneous Unsharp Masking for Bias Field Correction in MRI

摘要

A major issue in magnetic resonance (MR) image analysis is to remove the intensity inhomogeneity artifact present in MR images, which generally affects the performance of an automatic image analysis technique. In this context, the paper presents a novel approach for bias field correction in MR images by incorporating the merits of rough sets in estimating intensity inhomogeneity artifacts. Here, the concept of lower approximation and boundary region of rough sets deals with vagueness and incompleteness in filter structure definition and enables the algorithm to estimate optimum or near optimum bias field. A theoretical analysis is presented to justify the use of rough sets for bias field estimation. The performance of the proposed approach, along with a comparison with other bias field correction algorithms, is demonstrated on a set of MR images for different bias fields and noise levels.