Results 1  10
of
80
Cortical thickness analysis in autism with heat kernel smoothing
 NeuroImage
, 2005
"... We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their 3D Euclidean distance, has been widely used in 3D brain images to increase the signalton ..."
Abstract

Cited by 65 (23 self)
 Add to MetaCart
(Show Context)
We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their 3D Euclidean distance, has been widely used in 3D brain images to increase the signaltonoise ratio. When the observations lie on a convoluted brain surface, however, it is more natural to assign the weights based on the geodesic distance along the surface. We therefore develop a framework for geodesic distancebased kernel smoothing and statistical analysis on the cortical manifolds. As an illustration, we apply our methods in detecting the regions of abnormal cortical thickness in 16 high functioning autistic children via random field based multiple comparison correction that utilizes the new smoothing technique.
An Extension of the ICP Algorithm for Modeling Nonrigid Objects with Mobile Robots
"... The iterative closest point (ICP) algorithm [2] is a popular method for modeling 3D objects from range data. The classical ICP algorithm rests on a rigid surface assumption. Building on recent work on nonrigid object models [5, 16, 9] , this paper presents an ICP algorithm capable of modeling nonrig ..."
Abstract

Cited by 46 (6 self)
 Add to MetaCart
(Show Context)
The iterative closest point (ICP) algorithm [2] is a popular method for modeling 3D objects from range data. The classical ICP algorithm rests on a rigid surface assumption. Building on recent work on nonrigid object models [5, 16, 9] , this paper presents an ICP algorithm capable of modeling nonrigid objects, where individual scans may be subject to local deformations. We describe an integrated mathematical framework for simultaneously registering scans and recovering the surface configuration. To tackle the resulting...
Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage. 31:9931003
, 2006
"... (MACACC) using cortical thickness from MRI ..."
(Show Context)
A generic framework for parcellation of the cortical surface into gyri using geodesic Voronoi diagrams
, 2003
"... In this paper, we propose a generic automatic approach for the parcellation of the cortical surface into labeled gyri. These gyri are defined from a set of pairs of sulci selected by the user. The selected sulci are first automatically identified in the data, then projected onto the cortical surface ..."
Abstract

Cited by 38 (6 self)
 Add to MetaCart
In this paper, we propose a generic automatic approach for the parcellation of the cortical surface into labeled gyri. These gyri are defined from a set of pairs of sulci selected by the user. The selected sulci are first automatically identified in the data, then projected onto the cortical surface. The parcellation stems from two nested Vorono diagrams computed geodesically to the cortical surface. The first diagram provides the zones of influence of the sulci. The boundary between the two zones of influence of each selected pair of sulci stands for a gyrus seed. A second diagram yields the gyrus parcellation. The distance underlying the Vorono diagram allows the method to interpolate the gyrus boundaries where the limiting sulci are interrupted. The method is illustrated with twelve different hemispheres.
Smoothing and cluster thresholding for cortical surfacebased group analysis of fMRI data.
 Neuroimage
, 2006
"... Cortical surfacebased analysis of fMRI data has proven to be a useful method with several advantages over 3dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surfacebased analyses. Operating within the framework of the FreeSurfer soft ..."
Abstract

Cited by 30 (0 self)
 Add to MetaCart
(Show Context)
Cortical surfacebased analysis of fMRI data has proven to be a useful method with several advantages over 3dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surfacebased analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surfacebased version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surfacebased smoothing simulations and found a large disparity between the expected and actual kernel widths.
Diffusion Smoothing on Brain Surface via Finite Element Method
 In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI
, 2004
"... Surface data such as the segmented cortical surface of the human brain plays an important role in medical imaging. To increase the signaltonoise ratio for data residing on the brain surface, the data is usually diffused. Most of diffusion equation approach for triangulated mesh data is based on th ..."
Abstract

Cited by 28 (9 self)
 Add to MetaCart
Surface data such as the segmented cortical surface of the human brain plays an important role in medical imaging. To increase the signaltonoise ratio for data residing on the brain surface, the data is usually diffused. Most of diffusion equation approach for triangulated mesh data is based on the finite element method and a system of linear equations are iteratively solved without the explicit representation of the LaplaceBeltrami operator. Such implicit formulation requires inverting large sparse matrix that is required in most finite element methods.
Tensorbased cortical surface morphometry via weighted spherical harmonic representation
 IEEE Transactions on Medical Imaging
, 2008
"... ..."
(Show Context)
Implicit brain imaging
 NeuroImage
, 2004
"... We describe how implicit surface representations can be used to solve fundamental problems in brain imaging. This kind of representation is not only natural following the stateoftheart segmentation algorithms reported in the literature to extract the different brain tissues, but it is also, as sh ..."
Abstract

Cited by 16 (7 self)
 Add to MetaCart
(Show Context)
We describe how implicit surface representations can be used to solve fundamental problems in brain imaging. This kind of representation is not only natural following the stateoftheart segmentation algorithms reported in the literature to extract the different brain tissues, but it is also, as shown in this paper, the most appropriate one from the computational point of view. Examples are provided for finding constrained special curves on the cortex, such as sulcal beds, regularizing surface based measures, such as cortical thickness, and for computing warping fields between surfaces such as the brain cortex. All these result from efficiently solving partial differential equations and variational problems on surfaces represented in implicit form. The implicit framework avoids the need to construct intermediate mappings between 3D anatomical surfaces and parametric objects such planes or spheres, a complex step that introduces errors and is required by many other cortical processing approaches. 1
Tensorbased Brain Surface Modeling and Analysis
 in IEEE Conference on Computer Vision and Pattern Recognition
, 2003
"... We present a unified computational approach to tensorbased morphometry in detecting the brain surface shape difference between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis i ..."
Abstract

Cited by 15 (7 self)
 Add to MetaCart
(Show Context)
We present a unified computational approach to tensorbased morphometry in detecting the brain surface shape difference between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of the cortex may differ. It is highly likely that such surface shape differences are not uniform over the whole cortex. By computing how such surface metrics differ, the regions of the most rapid structural differences can be localized. To increase the signal to noise ratio, diffusion smoothing based on the explicit estimation of LaplaceBeltrami operator has been developed and applied to the surface metrics. As an illustration, we demonstrate how this new tensorbased surface 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.
Weighted Fourier series representation and its application to quantifying the amount of gray matter
 Special Issue of IEEE Transactions on Medical Imaging, on Computational Neuroanatomy
, 2007
"... representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a ..."
Abstract

Cited by 14 (4 self)
 Add to MetaCart
(Show Context)
representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a selfadjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at