A Review on MR Image Intensity Inhomogeneity Correction <s> Comparison between local and global statistics <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> Comparison between local and global statistics <s> Signal inhomogeneities in volumetric head MR scans are a major obstacle to segmentation and neuromorphometry. The fuzzy c-means (FCM) statistical clustering algorithm was extended to estimate and retrospectively correct a multiplicative inhomogeneity field in T 1 -weighted head MR scans. The method was tested on a mathematically simulated object and on seven whole head 3D MR scans. Once initial parameters governing operation of the algorithm were chosen for this class of images, results were obtained without intervention for individual MR studies. Post-acquisition inhomogeneity correction by extended FCM clustering improved overall image uniformity and separability of gray and white matter intensities. <s> BIB002 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Comparison between local and global statistics <s> We describe a computationally straightforward post-hoc statistical method of correcting spatially dependent image pixel intensity nonuniformity based on differences in local tissue intensity distributions. Pixel intensity domains for the various tissues of the composite image are identified and compared to the distributions of local samples. The nonuniformity correction is calculated as the difference of the local sample median from the composite sample median for the tissue class most represented by the sample. The median was chosen to reduce the effecters on determining the sample statistic and to allow a sample size small enough to accurately estimate the spatial variance of the image intensity nonuniformity. The method was designed for application to two-dimensional images. Simulations were used to estimate optimal conditions of local histogram kernel size and to test the accuracy of the method under known spatially dependent nonuniformities. The method was also applied to correct a phantom image and cerebral MRIs from 15 healthy subjects. Results show that the method accurately models simulated spatially dependent image intensity differences. Further analysis of clinical MR data showed that the variance of pixel intensities within the cerebral MRI slices and the variance of slice volumes within individuals were significantly reduced after nonuniformity correction. Improved brain-cerebrospinal fluid segmentation was also obtained. The method significantly reduced the variance of slice volumes within individuals, whether it was applied to the native images or images edited to remove nonbrain tissues. This statistical method was well behaved under the assumptions and the images tested. The general utility of the method was not determined, but conditions for testing the method under a variety of imaging sequences is discussed. We believe that this algorithm can serve as a method for improving MR image segmentation for clinical and research applications. <s> BIB003 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Comparison between local and global statistics <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> BIB004 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Comparison between local and global statistics <s> Abstract We describe a new magnetic resonance (MR) image analysis tool that produces cortical surface representations with spherical topology from MR images of the human brain. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The tools include skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability. <s> BIB005
There are efforts BIB002 BIB003 for a tissue and (2) that the intensity variation within a tissue is solely due to IIH. Not surprisingly, these methods are sensitive to the estimation of tissue statistics, which is nontrivial in practice. When GM and WM are combined as one class and the local statistic is estimated from a sample in a local region as done in BIB002 BIB003 , the method can be regarded as a generalized white matter method by Dawant et al. BIB001 , where the reference tissue is the combination of GM and WM. Also, it can be taken as a lowpass filtering estimation. Although it is usually much easier to identify GM and WM together than to identify WM alone, a potential problem is that the local sample could fail to adequately characterize the feature of the combined tissue class even though there is no artifact like IIH. A possible solution is to carry out more detail tissue classification in each local region. For example, the technique proposed in BIB004 BIB005 2 estimated the global tissue mean values by empirical thresholding, while the local statistics are derived through fitting the local histogram with a theoretical distribution. After obtaining estimations of local correction factors, a smooth function is fitted among these data and applied to the whole brain volume for IIH removal.