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:  LOWPASS FILTERING 
1. Abstract of Cited Paper (BIB001):  The use of surface coils in MR imaging has made it possible to obtain images of superficial structures with improved signal-to-noise ratio relative to conventional circumferential receiver coils [1 -4]. However, the signal intensity distribution in surface-coil images is inherently nonuniform; the decreased sensitivity to signal from more distant regions implies a conresponding decreased sensitivity to noise from those regions. As the total noise detected is uniformly distributed over the image, there is a net increase in the signal-to-noise ratio in the image of regions closer to the coil as compared with the conventional receiver coil [1 , 5]. The resulting range of relative intensity over surface-coil images makes it difficult to display them properly or to analyze them quantitatively. In addition, the local image contrast will decrease proportionally to the average local intensity. We report a method for correcting such surface-coil images so as to produce a uniform relative intensity over the region being imaged. 
2. Abstract of Cited Paper (BIB002):  An interactive computer method for quantifying CSF, white matter, and gray matter in magnetic resonance (MR) axial brain scans is presented. A stripping algorithm is used to remove the skull and scalp from each axial section. The images are then filtered to correct for radiofrequency inhomogeneity image artifacts. Late echo images are subtracted from or added to early echo images to enhance fluid/tissue and gray/white tissue contrast, respectively. Thresholds for fluid/tissue and gray/white separation are set interactively. A boundary pixel locking algorithm is used to handle ambiguities due to partial voluming between the fluid and tissue compartments. The MR brain scans from five healthy, young, normal men were obtained using a standard neuroanatomical reference technique. These data were processed and percentages computed for fluid, gray matter and white matter compartments. The gray/white ratios compare favorably with those determined in a published postmortem brain study. 
3. Abstract of Cited Paper (BIB003):  In this article, methods for measuring brain morphologic features on magnetic resonance images are described and normative data are provided for six morphologic variables. An estimated function relating age (ranging from 8 to 79 years) to average values is given for each measure. A linear decrease over the age range is observed in the volume of the cerebrum. Linear increases are observed in measures of ventricular and sulcal fluid. A curvilinear decrease in cortical volume is found and is demonstrable even in young adults. Highly nonlinear increases in the volume of signal hyperintensities are observed in cortical and subcortical regions. The methods described may be used to provide an age-adjusted index of morphologic abnormality for each subject on any of the measures. They are currently in use in ongoing neurobehavioral studies of patients with nonfocal brain abnormalities and primary disorders of affect and cognition. 
4. Abstract of Cited Paper (BIB004):  The volumes of total brain, white matter, gray matter, and cerebrospinal fluid in normal volunteers were determined using a supervised nonparametric feature map segmentation of dual-contrast MR images after correcting for the radio frequency (RF) inhomogeneity. Reproducibility of the segmentation was evaluated in terms of inter-observer and scan-to-scan variations. A comparison with the published results indicates that the scan-to-scan and inter-observer variabilities in the determination of tissue volumes are substantially reduced following the RF inhomogeneity correction. 
5. Abstract of Cited Paper (BIB005):  Two- and four-coil phased array detectors were developed to increase the sensitivity and resolution of MR imaging of the human brain cortex, especially for detecting cortical dysplasias in pediatric epilepsy patients. An automated intensity correction algorithm based on an edge-completed, low-pass filtered image was used to correct the image intensity for the inhomogenous reception profile of the coils. Seven phased array coils were constructed and tested. The sensitivity of these coils was up to 600% higher at the surface of the cortex than that achieved with a conventional head coil and up to 30% greater at the center of the head. The sensitivity obtained was comparable with that of a conventional small surface coil, but extended over the larger dimensions of the array and previously inaccessible areas such as the top of the head. The advantages of the improved sensitivity are demonstrated with high resolution images of the brain. 
6. Abstract of Cited Paper (BIB006):  Phased-array coils distribute the high signal-to-noise ratio (SNR) performance of their small component surface coils over the larger area covered by the entire array. The inhomogeneous sensitivity profiles of the component surface coils result in images with very high signal near the phased-array and decreased signal far from the array. This paper presents a postprocessing algorithm for correcting these coil-related intensity variations. The algorithm's performance was evaluated by correcting images of volunteers acquired with several different receive-only phased-array surface coils. 
7. Abstract of Cited Paper (BIB007):  A fast and robust method for correcting magnetic resonance image distortion due to field in homogeneity is proposed and applied to spiral k-space scanning. The method consists of acquiring a local field map, finding the best fit to a linear map, and using it to deblur the image distortions due to local frequency variations. The linear field map is determined using a maximum likelihood estimator with weights proportional to the pixel intensity. The method requires little additional computation and is robust in low signal regions and near abrupt field changes. Additionally. It can be used in combination with other deblurring methods. The application of this method is illustrated in conjunction with a mura'slice, T2-weighted, breath-held spiral scan of the liver. 
8. Abstract of Cited Paper (BIB008):  Grayscale inhomogeneities in magnetic resonance (MR) images confound quantitative analysis of these images. Homomorphic unsharp masking and its variations have been commonly used as a post-processing method to remove inhomogeneities in MR images, However, little data is available in the literature assessing the relative effectiveness of these algorithms to remove inhomogeneities, or describing how these algorithms can affect image data. In this study, the authors address these questions quantitatively using simulated images with artificially constructed and empirically measured bias fields. The authors' results show that mean-based filtering is consistently more effective than median-based algorithms for removing inhomogeneities in MR images, and that artifacts are frequently introduced into images at the most commonly used window sizes. The authors' results demonstrate dramatic improvement in the effectiveness of the algorithms with significantly larger windows than are commonly used. 
9. Abstract of Cited Paper (BIB009):  In this paper, a novel multi-scale method for coil sensitivity profile correction is presented based on wavelet transform. A magnetic resonance (MR) image can be decomposed into two spaces by the wavelet transform: approximate space and residual space. The approximate templates in approximate space can be thought of as multi-scale sensitivity profiles of the surface coil for coil correction. When we choose a suitable filter for decomposition, one of the sensitivity profiles should be optimal among the multi-scale sensitivity profiles. The optimal sensitivity profile can be chosen automatically by an analysis of the contents of the two spaces. The multi-scale method does not rely on any data other than the image generated by the MR scanner. The experiment showed promising results based on one-dimensional simulation and images of phantom and human images. J. Magn. Reson. Imaging 2001;13:428–436. © 2001 Wiley-Liss, Inc. 
10. Abstract of Cited Paper (BIB010):  Summary ::: ::: ::: Because of the inherent imperfections of the image formation process, microscopical images are often corrupted by spurious intensity variations. This phenomenon, known as shading or intensity inhomogeneity, may have an adverse affect on automatic image processing, such as segmentation and registration. Shading correction methods may be prospective or retrospective. The former require an acquisition protocol tuned to shading correction, whereas the latter can be applied to any image, because they only use the information already present in an image. Nine retrospective shading correction methods were implemented, evaluated and compared on three sets of differently structured synthetic shaded and shading-free images and on three sets of real microscopical images acquired by different acquisition set-ups. The performance of a method was expressed quantitatively by the coefficient of joint variations between two different object classes. The results show that all methods, except the entropy minimization method, work well for certain images, but perform poorly for others. The entropy minimization method outperforms the other methods in terms of reduction of true intensity variations and preservation of intensity characteristics of shading-free images. The strength of the entropy minimization method is especially apparent when applied to images containing large-scale objects. 
11. Abstract of Cited Paper (BIB011):  A new approach for correcting bias field in magnetic resonance (MR) images is proposed using the mathematical model of singularity function analysis (SFA), which represents a discrete signal or its spectrum as a weighted sum of singularity functions. Through this model, an MR image's low spatial frequency components corrupted by a smoothly varying bias field are first removed, and then reconstructed from its higher spatial frequency components not polluted by bias field. The thus reconstructed image is then used to estimate bias field for final image correction. The approach does not rely on the assumption that anatomical information in MR images occurs at higher spatial frequencies than bias field. The performance of this approach is evaluated using both simulated and real clinical MR images.

Literature Review:
Since the IIH is slowly varying in the image domain, its spectrum in frequency domain will be concentrated in the lowfrequency end. Therefore the IIH could be separated from the true image by a lowpass filter, L. After lowpass filtering in log-domain, one would approximately have This procedure to correct the IIH is similar to the homomorphic filtering in digital image processing for the correction of illumination inhomogeneity  . In fact, (1) easily reminds Zujun Hou 3 one of the illumination-reflectance model in optical imaging  , where the artifact from the illumination inhomogeneity is often termed "shading" in the literature. Thus, techniques for shading correction, such as the homomorphic filtering, can be adopted for IIH removal and the converse also holds. An investigation of applying IIH correction methods to deal with the shading problem in microscopic images has been carried out in BIB011 . Due to their simplicity and efficiency in implementation, lowpass filtering methods have been widely used  BIB011 BIB001 BIB002 BIB003 BIB004 BIB005 BIB006 BIB007  BIB008  BIB009  . For a summary, interested readers are referred to BIB009 . Also in BIB009 , the impact of filter width on IIH correction was investigated and it was found that these methods should be used with care to avoid intensity distortion and artificial artifacts in the corrected images. Basically, for MR images, due to the overlapping spectrum between the patient data and the IIH, the effectiveness of most conventional lowpass filtering in removing the IIH is generally quite limited. Luo et al. BIB012 presented a technique to recover lowfrequency components which correspond to anatomic structure and are lost during the lowpass filtering. The method expresses the signal with a linear combination of singularity functions. The higher-frequency components are assumed to be less affected by the IIH and are used to reconstruct the true image, after which the ratio between the observed and estimated image is used for IIH map approximation. Recently, lowpass filtering methods have been extended using the wavelet transform BIB010  and were shown to be effective in removing IIH in images acquired by surface coils and phase array coils. Compared with usual lowpass filtering methods, the multiresolution analysis allows one to select an optimal scale from which the approximate band in the wavelet transform domain is used for estimating the IIH map. In  , an improvement of a lowpass filtering method  was presented. The method varies the filter kernel size to minimize the segmentation error. The idea is generally similar to BIB010  in addressing IIH correction from the scale space, but differs in the criterion to determine the optimal scale.