A Review on MR Image Intensity Inhomogeneity Correction

Abstract

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Lowfrequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

Segmentation of Brain MRI

“…The retrospective methods are further classified into filtering, surface fitting, segmentation-based, and histogram-based, etc. Additionally, several valuable reviews about this topic can be found in (Arnold et al, 2001;Belaroussi et al, 2006;Hou, 2006;Sled et al, 1997;Velthuizen et al, 1998;Vovk et al, 2007). …”

Automatic Segmentation of Abdominal Adipose Tissue in MRI

“…A commonly used model for the bias field effect on a voxel with acquired intensity y observed is the multiplicative link [10] y observed = y true · y bias ⇔…”

Feature analysis of hyperpolarized helium‐3 pulmonary MRI: A study of asthmatics versus nonasthmatics

“…Such artifacts are potential confounders in application of advanced image analysis techniques. Much of the development for intensity inhomogeneity correction has been applied to MRI of neuroanatomy, where many techniques require a classification or segmentation step identifying the gray matter, white matter, and cerebrospinal fluid regions (16), a scenario that does not apply to helium-3 lung image data. With helium-3 image data, in addition to the amplitude of radiofrequency field, there are other potential contributions to intensity nonuniformity, such as the anatomic diffusion gradient (17) and posturerelated dependencies (18).…”

“…It has been shown that first-order statistics derived from the SFD image facilitate discriminative feature analysis in high-resolution CT images (16). An SFD image is produced by iterating through the original image where, at each voxel, the corresponding fractal value requires inspection of each pair of voxels in the surrounding neighborhood.…”