A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation. Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images than the second but that it is more sensitive to operator errors. > <s> BIB001 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> The specialised radiofrequency (RF) coils used in MRI such as head or surface coils can give rise to marked image intensity nonuniformities. There are two situations in which it is essential to correct this: (1) When a global intensity threshold is used to segment particular tissues; and (2) in proton density images, from which the proton concentration can be measured provided that the system gain is uniform or known over the whole image. We describe experiments to determine the magnitude and sources of nonuniformity in a 0.5-T system, and assess methods devised to correct for them in scans of any orientation, including oblique scans at arbitrary angles to the magnet axis and arbitrary offsets from the magnet iso-centre. A correction based on the response of a system to a uniform phantom was implemented. Tests of the correction with orthogonal views demonstrate that the uniformity of images of any orientation can be improved significantly with a correction matrix from just one orientation and still further with two matrices, one axial and the other either coronal or sagittal. We expect further improvements to be possible if gradient coil eddy current effects can be reduced. <s> BIB002 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, that has proven to be effective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal [three dimensional Fourier transform (3-DFT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil. The accuracy of adaptive segmentation was found to be comparable with manual segmentation, and closer to manual segmentation than supervised multivariant classification while segmenting gray and white matter. <s> BIB003 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> 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. <s> BIB004 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Intensity non-uniformity is an artifact often seen in MRI data that can significantly degrade automatic segmentation and prevent quantitative analysis. Using simulated MRI data, we have quantitatively compared the performance of three methods of correcting for intensity non-uniformity. In this analysis, novel stereotaxic space techniques were employed so that all three methods were fully automatic and capable of correcting volumes of any pulse sequence. Our results, based on correcting simulated T1, T2, and proton density (PD) weighted MRI scans with various levels of noise and non-uniformity, showed no method to be superior in all categories. However, the N3 technique distinguished itself for uniformly good performance without the limitation of assuming a sophisticated model of the data. <s> BIB005 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> We propose a modification of Wells et al. technique for bias field estimation and segmentation of magnetic resonance (MR) images. We show that replacing the class other, which includes all tissue not modeled explicitly by Gaussians with small variance, by a uniform probability density, and amending the expectation-maximization (EM) algorithm appropriately, gives significantly better results. We next consider the estimation and filtering of high-frequency information in MR images, comprising noise, intertissue boundaries, and within tissue microstructures. We conclude that post-filtering is preferable to the prefiltering that has been proposed previously. We observe that the performance of any segmentation algorithm, in particular that of Wells et al. (and our refinements of it) is affected substantially by the number and selection of the tissue classes that are modeled explicitly, the corresponding defining parameters and, critically, the spatial distribution of tissues in the image. We present an initial exploration to choose automatically the number of classes and the associated parameters that give the best output. This requires us to define what is meant by "best output" and for this we propose the application of minimum entropy. The methods developed have been implemented and are illustrated throughout on simulated and real data (brain and breast MR). <s> BIB006 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data. <s> BIB007 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Current MRI nonuniformity correction techniques are reviewed and investigated. Many approaches are used to remedy this artifact, but it is not clear which method is the most appropriate in a given situation, as the applications have been with different MRI coils and different clinical applications. In this work four widely used nonuniformity correction techniques are investigated in order to assess the effect on tumor response measurements (change in tumor volume over time): a phantom correction method, an image smoothing technique, homomorphic filtering, and surface fitting approach. Six braintumor cases with baseline and follow-up MRIs after treatment with varying degrees of difficulty of segmentation were analyzed without and with each of the nonuniformity corrections. Different methods give significantly different correction images, indicating that rf nonuniformity correction is not yet well understood. No improvement in tumor segmentation or in tumor growth/shrinkage assessment was achieved using any of the evaluated corrections. <s> BIB008 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> 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. <s> BIB009 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Inhomogeneity of the transmitted or received B1 field leads to intensity variations in MR images and spatial dependence in apparent concentration in MR spectra. We describe a simple method for investigating such variations. The transmitted B1 field can be measured both in vivo and in vitro which allows investigation of sample dependent effects that can not be measured on phantoms. For homogeneous regions the method also allows the received B1 field to be measured both in vivo and in vitro. Our method uses only a standard spin echo pulse sequence and simple region of interest analysis and should be implementable on any commercial scanner. The method is demonstrated using a variety of transmission and reception radiofrequency coils both in vivo and in vitro. <s> BIB010 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> A fully automated 3D version of the so-called white matter method for correcting intensity non-uniformity in MR T1- weighted neuro images is presented. The algorithm is an extension of the original work published previously. The major part of the extension was the development of a fully automated method for the generation of the reference points. In the design of this method, a number of measures were introduced to minimize the effects of possible inclusion of non-white matter voxels in the selection process. The correction process has been made iterative. A drawback of this approach is an increased cost in computational time. The algorithm has been tested on T1-weighted MR images acquired from a longitudinal study involving elderly subjects and people with probable Alzheimer's disease. More quantitative measures were used for the evaluation of the algorithm's performance. Highly satisfactory correction results have been obtained for images with extensive intensity non-uniformity either present in raw data or added artificially. With intensity correction, improved accuracy in the measurement of the rate of brain atrophy in Alzheimer's patients as well as in elderly people due to normal aging has been achieved.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB011 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Abstract The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods. <s> BIB012 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> Abstract We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average κ indices of κ = 0.746 ± 0.114 for gray matter (GM) and κ = 0.798 ± 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average κ indices κ = 0.893 ± 0.041 for GM and κ = 0.928 ± 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom. <s> BIB013 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> (2) Comparative study <s> 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. <s> BIB014
Compared to the numerous techniques for IIH correction, only a few studies have been carried out towards the comparative evaluation of existing algorithms. Sled et al. BIB005 have compared three IIH correction methods, the expectation maximization (EM) BIB003 , the white matter (WM) BIB001 , and the N3 method BIB007 using simulated T1, T2, and PD weighted data. It was shown that the WM method performs better than the other two methods for T1 weighted volumes, which might be due to the high contrast between the WM and other tissues in T1 weighted images. The EM method made excessively large corrections to voxels that fall outside the classifier's tissue model, as is consistent with that pointed out in BIB006 . Overall, the N3 method performs the most stable for all simulated images. Velthuizen et al. BIB008 have evaluated four IIH correction methods (a phantom method BIB002 , two lowpass filtering methods BIB004  , and a surface fitting method with reference points selected from white matter BIB001 ) in brain tumor segmentation. The surface fitting method was found to be inferior to others, which could be due to the way the reference points were generated. As mentioned in Section 3.1, the latter is crucial to the performance of the surface fitting method. An automatic method to generate such reference points has been presented in BIB011 . Hou and Huang  have also developed a similar technique based on order statistics, which is very well comparable with the state-of-the-art IIH correction methods. Although it turned out no improvement in tumor assessment after the IIH correction BIB008 , it does not mean that IIH correction is not an obstacle to automatic medical image processing in general, since the tumor segmentation is characterized by the localization of the tumor region as well as the intensity contrast with surrounded tissue. Thus, the tumor segmentation could be less affected by the IIH artifact. A more comprehensive study was presented in BIB012 , where six algorithms, n3 BIB007 , hum BIB009 , eq  , bfc BIB013 , spm (statistical parametric mapping) BIB010  , and cma 5 were compared against BrainWeb-simulated data as well as real volumes including repeated scans of the same subject, scans under different magnetic fields and different scanners. Three of the methods (hum, eq, and cma) are lowpass filtering based. The spm method is based on surface-fitting, and its parameters are estimated through integration with a tissue mixture model. It was found that the IIH maps obtained by filtering based methods can exhibit higher-frequency structures pertaining to brain anatomy. The spm method could be unstable when operating on relatively uniform image volumes and could lead to spurious solution for some volume. Overall, the n3 and the bfc methods are superior to the other four methods. At lower bias levels, the estimated bias by bfc is more accurate than that by n3, and at higher bias levels, the order reverses. Nevertheless, none of the six methods performs ideally under all the circumstances investigated. The problem of the spm might be similar to that of the EM method by Wells et al. BIB003 . Both methods utilized the mixture Gaussian classifier, which may be inadequate to model the image intensity distribution arising in practice. It should be pointed out that the spm method used in BIB012 is the SPM99 version, which has been updated to version SPM2 in 2003 with substantial improvement in theoretical modeling 8 International Journal of Biomedical Imaging or algorithmic design, and the latest version is SPM5. As to the three filtering-based methods, they lack a scheme to adapt the filtering strength to data quality, which may explain the inefficiency compared with the n3 and the bfc methods. As mentioned in Section 2, filtering methods BIB014   with data adaptivity have been developed recently, which might outperform their conventional counterparts. Although further comparative study using more extensive MR images is necessary, it might be inappropriate for end users to expect an algorithm superior to others and exclusively applicable. In general, each method has its underlying assumptions and limitations and the choice of which method to use is intimately interwined with the problem to solve, the source, and quality of the data. Many methods have attempted to correct the IIH artifact in brain MR images, some of which require the removal of the scalp/skull before the correction process, while others do not. Although sophisticated methods may be able to correct for IIH more accurately, one would also have to consider the expense of computer cost as well as the final segmentation error. Among the publicly available softwares, the N3 method has been widely used and its performance has been well demonstrated, while the BFC method can be advantageous when the image is also contaminated by severe noise  .