A Review on MR Image Intensity Inhomogeneity Correction <s> Integrated approaches <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> BIB001 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Integrated approaches <s> An icecraft has a base provided with a plurality of ice-engaging runners. A sail-surfing rig including an articulated mast carrying a triangular sail held in place by means of a bow-type boom is fitted to this support plate so that a user can stand on the support plate and ice sail in much the same manner as in surf sailing. A standard surfboard can constitute the base plate for such an icecraft, and the runners can be replaced by wheels or the like if desired for summer use of the arrangement on land. <s> BIB002 </s> A Review on MR Image Intensity Inhomogeneity Correction <s> Integrated approaches <s> Image intensity standardization is a postprocessing method designed for correcting acquisition-to-acquisition signal intensity variations (nonstandardness) inherent in magnetic resonance (MR) images. Inhomogeneity correction is a process used to suppress the low frequency background nonuniformities (inhomogeneities) of the image domain that exist in MR images. Both these procedures have important implications in MR image analysis. The effects of these postprocessing operations on improvement of image quality in isolation has been well documented. However, the combined effects of these two processes on MR images and how the processes influence each other have not been studied thus far. In this paper, we evaluate the effect of inhomogeneity correction followed by standardization and vice-versa on MR images in order to determine the best sequence to follow for enhancing image quality. We conducted experiments on several clinical and phantom data sets (nearly 4000 three-dimensional MR images were analyzed) corresponding to four different MRI protocols. Different levels of artificial nonstandardness, and different models and levels of artificial background inhomogeneity were used in these experiments. Our results indicate that improved standardization can be achieved by preceding it with inhomogeneity correction. There is no statistically significant difference in image quality obtained between the results of standardization followed by correction and that of correction followed by standardization from the perspective of inhomogeneity correction. The correction operation is found to bias the effect of standardization. We demonstrate this bias both qualitatively and quantitatively by using two different methods of inhomogeneity correction. We also show that this bias in standardization is independent of the specific inhomogeneity correction method used. The effect of this bias due to correction was also seen in magnetization transfer ratio (MTR) images, which are naturally endowed with the standardness property. Standardization, on the other hand, does not seem to influence the correction operation. It is also found that longer sequences of repeated correction and standardization operations do not considerably improve image quality. These results were found to hold for the clinical and the phantom data sets, for different MRI protocols, for different levels of artificial nonstandardness, for different models and levels of artificial inhomogeneity, for different correction methods, and for images that were endowed with inherent standardness as well as for those that were standardized by using the intensity standardization method. Overall, we conclude that inhomogeneity correction followed by intensity standardization is the best sequence to follow from the perspective of both image quality and computational efficiency. <s> BIB003
As seen from the description above, many IIH correction methods relate the problem with image segmentation and solve these two problems alternatively through an iteration process. Evidently, accurate segmentation would significantly ease the burden of IIH correction. Conversely, if the IIH has been precisely removed, the segmentation accuracy will in general be improved. Thus, it is not surprising to see overwhelming techniques addressing these two problems within a common framework. Actually, it has been a trend in computer vision to imitate the human intelligent system and solve the different problems simultaneously. A typical computer vision system may consist of several individual processes, which can be solved sequentially. However, the solution of one process could be beneficial to the solution of another one and the converse may also hold. As an example, image denoising and edge detection are two closely related problems. And it is common to require a denoising algorithm able to preserve image edge structures, and an edge detection method robust against noise. For the problem of IIH correction, beside the connection with image segmentation as mostly noted, there are efforts that relate the solution to image registration, because the image quality can impose an impact on the accuracy of image registration and conversely a good registration against a template could greatly help derive a high-quality image. In addition, there even exist attempts to address these three processes, IIH correction, segmentation, and registration together BIB002  . Among some processes, their relationship may not be very intimate, but different implementation order could result in different performance. In BIB001 , it was found that denoising after IIH correction is more preferable. Madabhushi and Udupa BIB003 investigated the interplay between IIH correction and intensity standardization, and concluded that the better sequence is IIH correction followed by intensity standardization.