Abstract—We describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach we take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself

Digital atlases of the brain serve as a spatial reference frame which can be used to relate data from different image modalities and experiments. In this paper we describe a standardized pipeline for the creation of extendable surface-based anatomical insect brain atlases from 3D image data of a

layout, a labeling of the image into geometric classes. Our main insight is to learn appearance-based models of these geometric classes, which coarsely describe the 3D scene orientation of each image region. Our multiple segmentation framework provides robust spatial support, allowing a wide variety

ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer’s

Abstract -We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from Introduction Techniques for labeling geometric features of the cerebral cortex are useful for analyzing a variety

measures of shape similarity and volumetric-overlap of these manually labeled regions were calculated when brains were aligned using a 12-parameter affine transform, SPM's nonlinear normalization, a diffeomorphic registration (ANTS), and FreeSurfer's surface-based registration. Registration error

selected reference image. An initial estimate of the probability of each possible disparity is made, based on the similarity of subimages surrounding the points. These estimates are iteratively improved by a relaxation labeling technique making use of the local continuity property of disparity that is a

corresponds to finding the non-rigid deformation between two surfaces, with applications to model-based object recognition and deformable object tracking. In previous work [1 ], we developed a fast and accurate technique for determining the rigid transformation between two surfaces, and also between a 3-D

� string-based techniques compare the surface form of the translation sentence to the surface form of the reference sentence(s) � criticised for their insensitivity to perfectly legitimate syntactic and lexical variation between the translation and the reference � almost all attempts at creating

Our main contributions in this paper are twofold. In the first instance, we demonstrate how H \Gamma K surface labelling can be realised using dictionary-based probabilistic relaxation. To facilitate this implementation we have developed a dictionary of feasible surface-label configurations