Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.

We propose a novel fully automated method for retrospective correction of intensity inhomogeneity, which is an undesired phenomenon in many automatic image analysis tasks, especially if quantitative analysis is the final goal. Besides most commonly used intensity features, additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic, so that local intensity variations can be corrected more efficiently. The proposed method is a four-step iterative procedure in which a non-parametric inhomogeneity correction is conducted. First, the probability distribution of image intensities and corresponding second derivatives is obtained. Second, intensity correction forces, condensing the probability distribution along the intensity feature, are computed for each voxel. Third, the inhomogeneity correction field is estimated by regularization of all voxel forces, and fourth, the corresponding partial inhomogeneity correction is performed. The degree of inhomogeneity correction dynamics is determined by the size of regularization kernel. The method was qualitatively and quantitatively evaluated on simulated and real MR brain images. The obtained results show that the proposed method does not corrupt inhomogeneity-free images and successfully corrects intensity inhomogeneity artefacts even if these are more dynamic.

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Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative analysis such as segmentation or registration. In this paper we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by combining the information from multiple MR channels. Intensity inhomogeneities are simultaneously removed in a four-step iterative procedure. First, the probability distribution of intensities for two channel images is calculated. In the second step, intensity correction forces, that tend to minimize image entropies, are estimated for every image voxel. Third, inhomogeneity correction fields are obtained by regularization and normalization of all voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images. The results show substantial improvement in comparison with the two state-of-the-art methods.

Abstract. In MRI, image intensity inhomogeneity is an adverse phenomenon that increases inter-tissue overlapping and hampers quantitative analysis. This study provides a powerful fully automated intensity inhomogeneity correction method that makes no a prior assumptions on the image intensity distribution and is able to correct intensity inhomogeneity with high dynamics. Besides using intensity features, as in most of the existing methods, spatial image features are also incorporated into the correction algorithm. A force is computed in each image point so that distribution of multiple features will shrink in the direction of intensity feature. Extensive regularization of those forces produces smooth inhomogeneity correction estimate, which is gradually improved in an iterative correction framework. The method was tested on simulated and real MR images for which gold standard segmentations were available. The results showed that the method was successful on uniform as well as on low and highly dynamic intensity uniformity images.