You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on MR Image Intensity Inhomogeneity Correction 
Chapter Title:  Image feature-based methods 
1. Abstract of Cited Paper (BIB001):  In this paper the problem of retrospective correction of intensity inhomogeneity in MR images is addressed. A novel model-based correction method is proposed, based on the assumption that an image corrupted by intensity inhomogeneity contains more information than the corresponding uncorrupted image. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. The degraded image is corrected by the inverse of the image degradation model. The parameters of this model are optimized such that the information of the corrected image is minimized while the global intensity statistic is preserved. The method was quantitatively evaluated and compared to other methods on a number of simulated and real MR images and proved to be effective, reliable, and computationally attractive. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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.

Literature Review:
An image feature-based technique was reported in BIB001 , where the IIH correction was decomposed into row and column correction. The correction factor at a voxel is firstly related to first-order difference at other voxels in the same row/column, then combined with those calculated in the rows/columns from the initial to the current one. The rationale underlying the computation is obscure from the description. However, it is very similar to that in  . In the latter technique, a smooth "variation" image was firstly derived from normalized intensity gradient field, where pixels with low intensity or high gradient magnitude are excluded. Then numerical integration was applied to the "variation" (first-order derivative) image to obtain an image, which only contains small variations and was used to determine the IIH map. Vovk et al. BIB003 BIB004 proposed to use the probability distribution of image features for IIH correction, where the image feature includes the intensity and the second spatial derivative of the image. Similar to the usual intensity histogram, the joint probability distribution also contains information for classifying tissues and such information was encoded by entropy. The correction factor was derived so that the entropy would decrease, similar to BIB002 .