A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> Due to the potential for essentially unbounded scene complexity, it is often necessary to translate the sensor-derived signals into richer symbolic representations. A key initial stage in this abstraction process is signal-level perceptual organization (SLPO) involving the processes of partitioning and identification. A parallel SLPO algorithm that follows the global hypothesis testing paradigm, but breaks the iterative structure of conventional region growing through the use of alpha -partitioning and region filtering is presented. These two techniques segment an image such that the gray-level variation within each region can be described by a regression model. Experimental results demonstrate the effectiveness of this algorithm. > <s> BIB001 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <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> BIB002 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> Medical imaging data sets are often corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The authors describe an automatic technique that not only improves the worst situations, such as those encountered with magnetic resonance imaging (MRI) surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets, such as MRI head scans, without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects, such as rf coil design, and patient dependent effects, such as attenuation of overlying tissue experienced both in high field MRI and X-ray computed tomography (CT). The authors show results for several MRI imaging situations including thorax, head, and breast. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation in overlying tissues. <s> BIB003 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. The authors have validated the bias correction algorithm on simulated data and they illustrate its performance on various MR images with important field inhomogeneities. They also relate the proposed algorithm to other bias correction algorithms. <s> BIB004 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <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> BIB005 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> This paper extends a previously reported theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is determined between every pair of image elements. This is done by considering all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. Although the theory allowed any neighborhood size for affinity definition, it did not indicate how this was to be selected. By bringing object scale into the framework in this paper, not only the size of the neighborhood is specified but also it is allowed to change in different parts of the image. This paper argues that scale-based affinity, and hence connectedness, is natural in object definition and demonstrates that this leads to more effective object segmentation.The approach presented here considers affinity to consist of two components. The homogeneity-based component indicates the degree of affinity between image elements based on the homogeneity of their intensity properties. The object-feature-based component captures the degree of closeness of their intensity properties to some expected values of those properties for the object. A family of non-scale-based and scale-based affinity relations are constructed dictated by how we envisage the two components to characterize objects. A simple and effective method for giving a rough estimate of scale at different locations in the image is presented. The original theoretical and algorithmic framework remains more-or-less the same but considerably improved segmentations result. The method has been tested in several applications qualitatively. A quantitative statistical comparison between the non-scale-based and the scale-based methods was made based on 250 phantom images. These were generated from 10 patient MR brain studies by first segmenting the objects, then setting up appropriate intensity levels for the object and the background, and then by adding five different levels for each of noise and blurring and a fixed slow varying background component. Both the statistical and the subjective tests clearly indicate that the scale-based method is superior to the non-scale-based method in capturing details and in robustness to noise. It is also shown, based on these phantom images, that any (global) optimum threshold selection method will perform inferior to the fuzzy connectedness methods described in this paper. <s> BIB006 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> This article is about bias field correction in MR brain images. In the literature, most of the methods consist in modeling the imaging process before identifying its unknown parameters. After identifying two of the most widely used such models, we propose a third one and show that for these three models, it is possible to use a common estimation framework, based on the Maximum Likelihood principle. This scheme partly rests on a functional modeling of the bias field. The optimization is performed by an ECM algorithm, in which we have included a procedure of outliers rejection. In this way, we derive three algorithms and compare them on a set of simulated images. We also provide results on real MR images exhibiting a bias field with a typical "diagonal" pattern. <s> BIB007 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms. <s> BIB008 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Segmentation <s> Scale is a fundamental concept useful in almost all image processing and analysis tasks including segmentation, filtering, interpolation, registration, visualization, and quantitative analysis. Broadly speaking, scale related work can be divided into three categories: (1) multi-scale or scale-space representation, (2) local scale, and (3) locally adaptive scale. The original formulation of scale in the form of scale-space theory came from the consideration of the presence of multiple scales in nature and the desire to represent measured signals at multiple scales. However, since this representation did not suggest how to select appropriate scales, the notion of local scale was proposed to pick the right scale for a particular application from the multi-scale representation of the image. Recently, there has been considerable interest in developing locally adaptive scales, the idea being to consider the local size of object in carrying out whatever local operations that are to be done on the image. However, existing locally adaptive models are limited by shape, size, and anisotropic constraints. In this work, we propose a generalized scale model which is spatially adaptive like other local morphometric models, and yet possesses the global spirit of multi-scale representations. We postulate that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other global and local scale formulations. We also present a variant of the generalized scale notion that we refer to as the generalized ball scale, which, in addition to having the advantages of the generalized scale model, also has superior noise resistance properties. Both scale models are dependent only on two parameters for their estimation, and we demonstrate means by which optimal values for these parameters can be estimated. Both the generalized scales can be readily applied to solving a range of image processing problems. One such problem that we address in this paper is correcting for slowly varying background spatial intensity in Magnetic Resonance images. We demonstrate the superiority of the generalized scale based inhomogeneity correction methods over an existing scale-based correction technique. <s> BIB009
A large category of surface fitting approaches search the parameters by fitting with respect to a set of tissue points encoding information about the IIH. Let I = {1, . . . , n} index the voxel coordinates of brain tissue. Then, in order to determine the parameters of this functional, one needs to find/segment a set of voxels S I ⊆ I which convey information about the IIH map. Among these methods, the essential difference lies in the identification of S I , and hence decoding the IIH information from S I . Dawant et al. BIB002 proposed to manually select S I such that they belong to the same type of tissue. As a result, the intensity variation among these voxels can largely be attributed to IIH. However, expert supervision to select points is time consuming and error prone, especially for volume data. Wang et al. BIB005 has presented an automated method for generating the reference points. Meyer et al. BIB003 employed the LCJ method BIB001 to preliminarily segment the image and then fit a smooth functional over the segmented image. The LCJ segmentation method assumes the image to be piecewise smooth and requires that the different objects are well separated at the boundaries, which is quite stringent in practice, particularly when image quality is poor due to perturbation such as noise, partial volume artifact, or IIH. Beside that, not every clinical department can afford the computer cost to run the parallel LCJ algorithm. Liew and Yan BIB008 approximated the IIH as a stack of Bspline surfaces with continuity constraints across slices. The estimation of IIH interwines with a fuzzy c-means clustering process. In BIB006 BIB009 , segmentation that utilizes local scale as homogeneous criteria has been presented and applied to IIH correction. When a statistical classifier, such as Gaussian classifier or random field modeling, is exploited BIB004  BIB007 , the process is similar to the parameter estimation in Section 4, where the parameters are associated with a probability distribution and can be estimated with common statistical estimation methods such as maximum-likelihood estimation.