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160
ObjectBased Morphometry of the Cerebral Cortex
 IEEE Trans. On Medical Imaging
, 2004
"... Most of the approaches dedicated to automatic morphometry rely on a pointbypoint strategy based on warping each brain towards a reference coordinate system. In this paper, we describe an alternative objectbased strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist ..."
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Cited by 28 (3 self)
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Most of the approaches dedicated to automatic morphometry rely on a pointbypoint strategy based on warping each brain towards a reference coordinate system. In this paper, we describe an alternative objectbased strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.
Diffeomorphic statistical shape models
 In British Machine Vision Conference
, 2004
"... We describe a method of constructing parametric statistical models of shape variation which can generate continuous diffeomorphic (nonfolding) deformation £elds. Traditional statistical shape models are constructed by analysis of the positions of a set of landmark points. Here we analyse the parame ..."
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Cited by 24 (2 self)
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We describe a method of constructing parametric statistical models of shape variation which can generate continuous diffeomorphic (nonfolding) deformation £elds. Traditional statistical shape models are constructed by analysis of the positions of a set of landmark points. Here we analyse the parameters of continuous warp £elds, constructed by composing simple parametric diffeomorphic warps. The warps are composed in such a way that the deformations are always de£ned in a reference frame. This allows the parameters controlling the deformations to be meaningfully compared from one example to another. A linear model is learnt to represent the variations in the warp parameters across the training set. This model can then be used to generalise the deformations. Models can be built either from sets of annotated points, or from unlabelled images. In the latter case, we use techniques from nonrigid registration to construct the warp £elds deforming a reference image into each example. We describe the technique in detail and give examples of the resulting models. 1
Support Vector Machines for 3D Shape Processing
, 2005
"... We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parame ..."
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Cited by 22 (5 self)
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We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SV Machine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.
Mumford–Shah Model for OnetoOne Edge Matching
"... Abstract—This paper presents a new algorithm based on the Mumford–Shah model for simultaneously detecting the edge features of two images and jointly estimating a consistent set of transformations to match them. Compared to the current asymmetric methods in the literature, this fully symmetric metho ..."
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Cited by 21 (2 self)
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Abstract—This paper presents a new algorithm based on the Mumford–Shah model for simultaneously detecting the edge features of two images and jointly estimating a consistent set of transformations to match them. Compared to the current asymmetric methods in the literature, this fully symmetric method allows one to determine onetoone correspondences between the edge features of two images. The entire variational model is realized in a multiscale framework of the finite element approximation. The optimization process is guided by an estimation minimizationtype algorithm and an adaptive generalized gradient flow to guarantee a fast and smooth relaxation. The algorithm is tested on T1 and T2 magnetic resonance image data to study the parameter setting. We also present promising results of four applications of the proposed algorithm: interobject monomodal registration, retinal image registration, matching digital photographs of neurosurgery with its volume data, and motion estimation for frame interpolation. Index Terms—Image registration, edge detection, Mumford– Shah (MS) model. Fig. 1. Nonsymmetric MS model for edge matching. and are the given reference and template images. and are the restored, piecewise smooth functions of image and image. is the combined discontinuity set of both images. Function represents the spatial transformation from image to image. I.
Measuring Geodesic Distances on the Space of Bounded Diffeomorphisms
 In BMVC
, 2002
"... This paper considers the problem of measuring the differences between deformations. ..."
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Cited by 20 (7 self)
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This paper considers the problem of measuring the differences between deformations.
Neuroanatomical differences between mouse strains as shown by highresolution 3D MRI
, 2005
"... The search for new mouse models of human disease requires a sensitive metric to make threedimensional (3D) anatomical comparisons in a rapid and quantifiable manner. This is especially true in the brain, where changes in complex shapes such as the hippocampus and ventricles are difficult to assess ..."
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Cited by 16 (5 self)
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The search for new mouse models of human disease requires a sensitive metric to make threedimensional (3D) anatomical comparisons in a rapid and quantifiable manner. This is especially true in the brain, where changes in complex shapes such as the hippocampus and ventricles are difficult to assess with 2D histology. Here, we report that the 3D neuroanatomy of three strains of mice (129S1/ SvImJ, C57/Bl6, and CD1) is significantly different from one another. Using image coregistration, we Fmorphed _ together nine brains of each strain scanned by magnetic resonance imaging at (60 Am) 3 resolution to synthesize an average image. We applied three methods of comparison. First, we used visual inspection and graphically examined the standard deviation of the variability in each strain. Second, we annotated 42 neural structures and compared their volumes across the strains. Third, we assessed significant local deviations in volume and displacement between the two inbred strains, independent of prior anatomical knowledge. D 2005 Elsevier Inc. All rights reserved.
Object correspondence as a machine learning problem
 In Proceedings of the 22nd International Conference on Machine Learning (ICML 05
, 2005
"... We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points of one ..."
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Cited by 15 (5 self)
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We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points of one object will be mapped to “similar ” points on the other one. Our system, which contains little engineering or domain knowledge, delivers state of the art performance. We present application results including close to photorealistic morphs of 3D head models. 1.
Leftinvariant Riemannian elasticity: a distance on shape diffeomorphisms
, 2006
"... Abstract. In intersubject registration, one often lacks a good model of the transformation variability to choose the optimal regularization. Some works attempt to model the variability in a statistical way, but the reintroduction in a registration algorithm is not easy. In [1], we interpreted the ..."
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Cited by 15 (5 self)
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Abstract. In intersubject registration, one often lacks a good model of the transformation variability to choose the optimal regularization. Some works attempt to model the variability in a statistical way, but the reintroduction in a registration algorithm is not easy. In [1], we interpreted the elastic energy as the distance of the GreenSt Venant strain tensor to the identity. By changing the Euclidean metric for a more suitable Riemannian one, we defined a consistent statistical framework to quantify the amount of deformation. In particular, the mean and the covariance matrix of the strain tensor could be efficiently computed from a population of nonlinear transformations and introduced as parameters in a Mahalanobis distance to measure the statistical deviation from the observed variability. This statistical Riemannian elasticity was able to handle anisotropic deformations but its isotropic stationary version was locally inverseconsistent. In this paper, we investigate how to modify the Riemannian elasticity to make it globally inverse consistent. This allows to define a leftinvariant ”distance ” between shape diffeomorphisms that we call the leftinvariant Riemannian elasticity. Such a closed form energy on diffeomorphisms can optimize it directly without relying on a time and memory consuming numerical optimization of the geodesic path. 1
A Continuous STAPLE for Scalar, Vector and Tensor Images: An Application to DTI Analysis
, 2009
"... The comparison of images of a patient to a reference standard may enable the identification of structural brain changes. These comparisons may involve the use of vector or tensor images (i.e. 3D images for which each voxel can be represented as an RN vector) such as Diffusion Tensor Images (DTI) or ..."
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Cited by 14 (4 self)
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The comparison of images of a patient to a reference standard may enable the identification of structural brain changes. These comparisons may involve the use of vector or tensor images (i.e. 3D images for which each voxel can be represented as an RN vector) such as Diffusion Tensor Images (DTI) or transformations. The recent introduction of the LogEuclidean framework for diffeomorphisms and tensors has greatly simplified the use of these images by allowing all the computations to be performed on a vectorspace. However, many sources can result in a bias in the images, including disease or imaging artifacts. In order to estimate and compensate for these sources of variability, we developed a new algorithm, called continuous STAPLE, that estimates the reference standard underlying a set of vector images. This method, based on an ExpectationMaximization method similar in principle to the validation method STAPLE, also estimates for each image a set of parameters characterizing their bias and variance with respect to the reference standard. We demonstrate how to use these parameters for the detection of atypical images or outliers in the population under study. We identified significant differences between the tensors of diffusion images of multiple sclerosis patients and those of control subjects in the vicinity of lesions.
Constructing DataDriven Optimal Representations for Iterative Pairwise NonRigid Registration
, 2003
"... Nonrigid registration of a pair of images depends on the generation of a dense deformation field across one of the images. Such deformation fields can be represented by the deformation of a set of knotpoints, interpolated to produce the continuous deformation field. ..."
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Cited by 13 (8 self)
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Nonrigid registration of a pair of images depends on the generation of a dense deformation field across one of the images. Such deformation fields can be represented by the deformation of a set of knotpoints, interpolated to produce the continuous deformation field.