Results 1 - 10 of 443

probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM

by John Mazziotta, Arthur Toga, Alan Evans, Peter Fox, Jack Lancaster, Karl Zilles, Roger Woods, Tomas Paus, Gregory Simpson, Bruce Pike, Colin Holmes, Louis Collins, Paul Thompson, David Macdonald, Marco Iacoboni, Thorsten Schormann, Katrin Amunts, Nicola Palomero-gallagher, Stefan Geyer, Larry Parsons, Katherine Narr, Noor Kabani, Georges Le Goualher, Dorret Boomsma, Tyrone Cannon, Ryuta Kawashima, Bernard Mazoyer - P, MACDONALD D, IACOBONI M, SCHORMANN T, AMUNTS K, PALOMERO-GALLAGHER N, GEYER S, PARSONS L, NARR K, KABANI N, LE GOUALHER G, BOOMSMA D, CANNON T, KAWASHIMA R and MAZOYER B. A , 2001
"... Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital ..."
Abstract - Cited by 208 (35 self) - Add to MetaCart
Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital
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Automated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex

by Bruce Fischl, Arthur Liu, Anders M. Dale , 2001
"... Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. E ..."
Abstract - Cited by 167 (25 self) - Add to MetaCart
Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
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Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

by Arno Klein , Jesper Andersson , Babak A. Ardekani , John Ashburner , Brian Avants , et al. - NEUROIMAGE 46 (2009) 786–802 , 2009
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Abstract - Cited by 159 (13 self) - Add to MetaCart
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Magnetic resonance image tissue classification using a partial volume model

by David W. Shattuck, Stephanie R. Sandor-Leahy, Kirt A. Schaper, David A. Rottenberg, Richard M. Leahy - NEUROIMAGE , 2001
"... 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 imag ..."
Abstract - Cited by 137 (6 self) - Add to MetaCart
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 � indices of ��0.746 � 0.114 for gray matter (GM) and ��0.798 � 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average � indices �� 0.893 � 0.041 for GM and ��0.928 � 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute’s BrainWeb phantom.

A hybrid approach to the skull stripping problem in MRI

by F. Ségonne, A. M. Dale, B E. Busa, B M. Glessner, B D. Salat, B H. K. Hahn, B. Fischl A - NeuroImage , 2004
"... We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single whit ..."
Abstract - Cited by 127 (11 self) - Add to MetaCart
We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skullstripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skullstripping tools.
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Sequence-independent segmentation of magnetic resonance images

by Bruce Fischl, David H. Salat, A Nikos Makris, Florent Ségonne, B Brian T. Quinn, Anders M. Dale A - Neuroimage , 2004
"... We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contras ..."
Abstract - Cited by 112 (20 self) - Add to MetaCart
We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2 * and of reducing test–retest intensity variability.
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Deformation-Based Surface Morphometry Applied to Gray Matter Deformation

by Keith J. Worsley, Steve Robbins, Tomas Paus, Jonathan Taylor, Jay N. Giedd, Judith L. Rapoport, Alan C. Evans, Moo K. Chung, Moo K. Chung , 2003
"... We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature and total gray matter volume change. It i ..."
Abstract - Cited by 80 (32 self) - Add to MetaCart
We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature and total gray matter volume change. It is highly likely that such age-related surface changes are not uniform. By measuring how such surface metrics change over time, the regions of the most rapid structural changes can be localized. We avoided using surface flattening, which distorts the inherent geometry of the cortex in our analysis and it is only used in visualization. To increase the signal to noise ratio, di#usion smoothing, which generalizes Gaussian kernel smoothing to an arbitrary curved cortical surface, has been developed and applied to surface data. Afterwards, statistical inference on the cortical surface will be performed via random fields theory. As an illustration, we demonstrate how this new surface-based morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children and adolescents.

Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

by Xiao Han, Chenyang Xu, Ulisses Braga-neto, Jerry L. Prince, Senior Member - IEEE Trans. Med. Imaging , 2002
"... Abstract — Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that l ..."
Abstract - Cited by 66 (7 self) - Add to MetaCart
Abstract — Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully-automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.
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Meta-analysis of functional neuroimaging data: Current and future directions

by Ryan Yue, Martin A. Lindquist, Ji Meng Loh - Social Cognitive and Affective Neuroscience , 2007
"... ar ..."
Abstract - Cited by 63 (7 self) - Add to MetaCart
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Computational anatomy: Shape, growth, and atrophy comparison via diffeomorphisms

by Michael I. Miller - NeuroImage , 2004
"... Computational anatomy (CA) is the mathematical study of anatomy I a I = I a BG, an orbit under groups of diffeomorphisms (i.e., smooth invertible mappings) g a G of anatomical exemplars Iaa I. The observable images are the output of medical imaging devices. There are three components that CA examine ..."
Abstract - Cited by 62 (2 self) - Add to MetaCart
Computational anatomy (CA) is the mathematical study of anatomy I a I = I a BG, an orbit under groups of diffeomorphisms (i.e., smooth invertible mappings) g a G of anatomical exemplars Iaa I. The observable images are the output of medical imaging devices. There are three components that CA examines: (i) constructions of the anatomical submanifolds, (ii) comparison of the anatomical manifolds via estimation of the underlying diffeomorphisms g a G defining the shape or geometry of the anatomical manifolds, and (iii) generation of probability laws of anatomical variation P(d) on the images I for inference and disease testing within anatomical models. This paper reviews recent advances in these three areas applied to shape, growth, and atrophy.
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