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114
Consistent Image Registration
, 2001
"... This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registr ..."
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Cited by 103 (6 self)
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This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and Xray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linearelasticity give better registration results than using either constraint alone or none at all.
Statistical Models of Appearance for Medical Image Analysis and Computer Vision
 In Proc. SPIE Medical Imaging
, 2001
"... Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the te ..."
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Cited by 92 (1 self)
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Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the texture variation and a model of the correlations between shape and texture. With enough training examples such models should be able to synthesize any image of normal anatomy. By finding the parameters which optimize the match between a synthesized model image and a target image we can locate all the structures represented by the model. Two approaches to the matching will be described. The Active Shape Model essentially matches a model to boundaries in an image. The Active Appearance Model finds model parameters which synthesize a complete image which is as similar as possible to the target image. By using a `difference decomposition' approach the current difference between target image and synthesi...
On the Geometry and Shape of Brain SubManifolds
 International Journal of Pattern Recognition and Artificial Intelligence
, 1997
"... This paper develops mathematical representations for neuroanatomically significant substructures of the brain and their variability in a population. The focus of the paper is on the neuroanatomical variation of the geometry and the "shape" of 2dimensional surfaces in the brain. As examp ..."
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Cited by 73 (9 self)
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This paper develops mathematical representations for neuroanatomically significant substructures of the brain and their variability in a population. The focus of the paper is on the neuroanatomical variation of the geometry and the "shape" of 2dimensional surfaces in the brain. As examples, we focus on the cortical and hippocampal surfaces in an ensemble of Macaque monkeys and human MRI brains. The "shape" of the substructures are quantified via the construction of templates; the variations are represented by defining probabilistic deformations of the template. Methods for empirically estimating probability measures on these deformations are developed by representing the deformations as Gaussian random vector fields on the embedded submanifolds. This work was supported by NIH grants RR01380, RO1MH5215801A1 , ARO DAAL0386K0110, ARO DAAH049510494 and NSF grant BIR9424264 The Gaussian random vector fields are constructed as quadratic mean limits using complete orthonormal b...
Consistent Landmark and IntensityBased Image Registration
, 2002
"... Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near ..."
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Cited by 62 (2 self)
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Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near landmark locations by matching corresponding landmarks and away from landmark locations by matching the image intensities. In contrast to similar unidirectional algorithms, these new consistent algorithms jointly estimate the forward and reverse transformation between two images while minimizing the inverse consistency error  the error between the forward (reverse) transformation and the inverse of the the reverse (forward) transformation. This reduces the ambiguous correspondence between the forward and reverse transformations associated with large inverse consistency errors. In both algorithms a thinplate spline (TPS) model is used to regularize the estimated transformations. Twodimensional (2D) examples are presented that show the inverse consistency error produced by the traditional unidirectional landmark TPS algorithm can be relatively large and that this error is minimized using the consistent landmark algorithm. Results using 2D magnetic resonance imaging data are presented that demonstrate that using landmark and intensity information together produce better correspondence between medical images than using either landmarks or intensity information alone.
Diffeomorphic Demons: Efficient Nonparametric Image Registration
, 2008
"... We propose an efficient nonparametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. In the first part of this paper, we show that Thirion’s demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theor ..."
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Cited by 59 (8 self)
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We propose an efficient nonparametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. In the first part of this paper, we show that Thirion’s demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion’s demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.
Fast parametric elastic image registration
 IEEE Transactions on Image Processing
, 2003
"... Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of so ..."
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Cited by 59 (4 self)
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Abstract—We present an algorithm for fast elastic multidimensional intensitybased image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard realworld problems, it is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. Particular attention has been paid to the factors influencing the speed of this algorithm. The Bspline deformation model is shown to be computationally more efficient than other alternatives. The algorithm has been successfully used for several twodimensional (2D) and threedimensional (3D) registration tasks in the medical domain, involving MRI, SPECT, CT, and ultrasound image modalities. We also present experiments in a controlled environment, permitting an exact evaluation of the registration accuracy. Test deformations are generated automatically using a random hierarchical fractional waveletbased generator. Index Terms—Elastic registration, image registration, landmarks, splines. I.
Voxelbased morphometry using the ravens maps: Methods and validation using simulated longitudinal atrophy
 NeuroImage
, 2001
"... Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in populationbased studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extensi ..."
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Cited by 57 (17 self)
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Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in populationbased studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extension and validation of a previously published methodology, referred to as RAVENS, for characterizing regional atrophy in the brain. A new method for elastic, volumepreserving spatial normalization, which allows for accurate quantification of very localized atrophy, is used. The RAVENS methodology was tested on images with simulated atrophy within two gyri: precentral and superior temporal. It was found to accurately determine the regions of atrophy, despite their localized nature and the interindividual variability of cortical structures. Moreover, it was found to perform substantially better than the voxelbased morphology method of SPM’99. Improved sensitivity was achieved at the expense of human effort involved in defining a number of sulcal curves that serve as constraints on the 3D elastic warping. © 2001 Academic Press
A Unified Framework for Atlas Matching using Active Appearance Models
, 1999
"... We propose to use statistical models of shape and texture as deformable anatomical atlases. By training on sets of labelled examples these can represent both the mean structure and appearance of anatomy in medical images, and the allowable modes of deformation. Given enough training examples such a ..."
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Cited by 56 (2 self)
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We propose to use statistical models of shape and texture as deformable anatomical atlases. By training on sets of labelled examples these can represent both the mean structure and appearance of anatomy in medical images, and the allowable modes of deformation. Given enough training examples such a model should be able synthesise any image of normal anatomy. By finding the parameters which minimise the difference between the synthesised model image and the target image we can locate all the modelled structure. This potentially time consuming step can be solved rapidly using the Active Appearance Model (AAM). In this paper we describe the models and the AAM algorithm and demonstrate the approach on structures in MR brain crosssections.
3D Statistical Shape Models Using Direct Optimisation of Description Length
, 2002
"... We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a tran ..."
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Cited by 55 (4 self)
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We describea n a26`('9b method for buildingoptima 3D sta22j9b'2 sha e models from sets oftraj'Hj sha es. Althoughsha e models showconsideraj promisea a bami for segmentingan interpreting imainga ma jordra wba k of theae9`2j h is the need toestaH69 a dense correspondenceadenc a trance9 set ofexa')( sha es. It is importa t to esta)`9b the correct correspondence, otherwise poor models ca result. In 2D, thisca be a hieved usingma ua `la9`'H`9b but in 3D this becomesimpra2`269 We show it is possible toesta6jH9 correspondences automatically, byca6)22 the correspondence problema one of finding the`optima) paima)9b`2'2)9 of ea hsha e in thetra'22 set. We describea n explicit representares ofsurfa6 paa6(9b`j"`9a tha ensures the resulting correspondencesad legac ag show how this representaen9ca bemaH('9b2)j to minimise thed933J292 length of the tra'H22 set using the model. This results incompaH models with good generab2('H9 properties. Resultsas reported for two sets ofbiomedica sha es, showingsignifica t improvement in model propertiescompa9' to thoseobta9j) usinga uniform surfam paam92))559b2'6 1