At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. © 2000 Academic Press

Abstract. An algorithm for improved automatic segmentation of gross anatomical structures of the human brain is presented that merges the output of a tissue classification process with gross anatomical region masks, automatically defined by non-linear registration of a given data set with a probabilistic anatomical atlas. Experiments with 20 real MRI volumes demonstrate that the method is reliable, robust and accurate. Manually and automatically defined labels of specific gyri of the frontal lobe are similar, with a Kappa index of 0.657. 1

Registering medical images of different individuals is difficult due to inherent anatomical variabilities and possible pathologies. This thesis focuses on characterizing non-pathological variations in human brain anatomy, and applying such knowledge to achieve accurate 3D deformable registration. Inherent anatomical variations are automatically extracted by deformably registering training data with an expert-segmented 3-D image, a digital brain atlas. Statistical properties of the density and geometric variations in brain anatomy are measured and encoded into the atlas to build a statistical atlas. These statistics can function as prior knowledge to guide the automatic registration process. Compared to an algorithm with no knowledge guidance, registration using the statistical atlas reduces the overall error on 40 test cases by 34%. Automatic registration between the atlas and a subject's data adapts the expert segmentation for the subject, thus reduces the months-long manual segmentation process to minutes. Accurate and efficient segmentation of medical images enable quantitative study of anatomical differences between populations, as well as detection of abnormal variations indicative of pathologies.

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Voxel-based morphometry (VBM) is a powerful tool for analyzing changes in gray or white matter density of the brain. By using an automated segmentation procedure and standardized parametric statistics it avoids biases inherent in operator-dependent morphological operations (J. Ashburner and K. J. Friston, 2000, NeuroImage 11, 805–821). Since its introduction in 1995, VBM has been used to examine anatomical changes in a variety of diseases associated with neurologic and psychiatric dysfunction. Given the power of this technique for discerning subtle anatomical changes, we wanted to assess its performance on brains with gross structural abnormalities. Such results could have implications regarding the difficulties to be faced when examining other types of distorted

This paper offers the first comprehensive characterization of the cognitive diversity of individual brain regions. The results suggest that individual brain regions—even fairly small regions—contribute to multiple tasks across different cognitive-emotional domains, and moreover that there is little difference in diversity between cortical and sub-cortical circuits.

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A mixture-mo del clusteringalgoteri is presented fo rozS[ MRI brain image segmentatio in the presenceo partial voESE averaging. The metho d uses additioqG classes to represent partial voHBz vo xelso mixed tissue type in the image. Pro/[4[qG y distributioS fo partial vo/]H vo xels are mo deledaccozE]z3q . The imagemo delalso allo ws fo tissue-dependent variance values and vo xel neighbo rho o dinfozqGHz/ is taken into accoB t in the clustering fol ulatioe AdditioGH/S we extend the imagemo delto accoE t fo a lo w frequency intensity inho]HqGH/S y that may be present in an image. This so/Bz44q shading e#ect ismo deled as a linear cobinatio o poHESz3qG basis functio]S and is estimated within the clusteringalgoingq/ Wealso investigate the poHzz3SqG yo f usingadditioHq anatoioH prio infoioHqG onfoio by registering tissue class template images to the imageto be segmented. The final result is the estimated fractioqG amoi t o each tissue type present within a vo xel inadditio to the label assignedto the vo xel. A parallel implementatio o the metho d is evaluated using synthetic and real MRI data.

This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate withinmodality registrations to two template images that are already in register. One can use a least-squares minimization to determine the affine transformations that map between the templates and the images. By incorporating suitable constraints, a rigid body transformation which directly maps between the images can be extracted from these more general affine transformations. A further refinement capitalizes on the implicit normalization of both images into a standard space. This facilitates segmentation or partitioning of both original images into homologous tissue classifications. Once partitioned, the partitions can be jointly matched, further increasing the accuracy of the coregistration. In short, these techniques reduce the between-modality problem to a series of simpler within-modality problems. These methods are relatively robust, address a number of problems in image transformations, and require no manual intervention.

Abstract. In this paper, we propose a local likelihood-based active contour model for subcortical structure segmentation. As a generalization of the Chan-Vese piecewise-constant model, our solution uses Bayesian a posterior probabilities as the driving forces for curve evolution. Distribution prior for the structure of interest, e.g., caudate nucleus, can be seamlessly integrated into the level set evolution procedure, and no thresholding step is needed for capturing the target. Unlike other region-based active contour models, our solution relaxes the global piecewise-constant assumption, and uses locally varying Gaussians to better account for intensity inhomogeneity and local variations existing in many MR images. More accurate and robust segmentations are therefore achieved. 1