Inhomogeneity Correction for Brain Magnetic Resonance Images by Rank Leveling

Abstract

A postprocessing method of rank filtering inhomogeneity correction using nonlinear rank filtering of magnetic resonance imaging (MRI) scans is described. The method addresses some of the problems of homomorphic unsharp masking (HUM) using mean or median filtering. Maximum rank filtering was used to estimate the bias image, which was then smoothed and used to normalize the original image. The coefficient of variation within and between tissue classes before and after inhomogeneity correction was calculated in simulated brain phantom images and clinical T1-weighted MRI images. Comparison was made with mean filter-based and median filter-based HUM. Maximum rank filtering reduced within and between class coefficients of variation. Performance of median filtering was inferior to that of mean filtering, and both were inferior to performance of maximum rank filtering. The method is easy to implement and is effective against different bias types. It is less prone to edge effects than mean and median filtering.