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Imagematching as a diffusion process: an analogy with Maxwell’s demons (1998)

by J-P Thirion
Venue:Medical Image Analysis
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Image registration methods: a survey.

by Barbara Zitová , Jan Flusser , 2003
"... Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrical ..."
Abstract - Cited by 760 (10 self) - Add to MetaCart
Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q
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...ow based registration. The diffusion registration handles object contours and other features as membranes, setting the geometrical constraints. Three variations of this approach are described in Ref. =-=[189]-=-. Different solution was proposed by Andersen and Nielsen [5]. Vemuri et al. [199] introduced elastic registration method, based on evolution of level sets, moving along their respective normals. Fina...

Nonrigid registration using free-form deformations: Application to breast MR images

by D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, D. J. Hawkes - IEEE Transactions on Medical Imaging , 1999
"... Abstract — In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion i ..."
Abstract - Cited by 697 (36 self) - Add to MetaCart
Abstract — In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. I.
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...owever, most of these approaches are limited either to rigid or affine transformations. In contrast, many nonrigid registration algorithms based on elastic deformations, such as animal [16] or demons =-=[17]-=-, rely on the assumption that the intensity of tissues between images remains constant. This is also true for nonrigid registration algorithms based on fluid deformations [18], [19]. A notable excepti...

Statistical Models of Appearance for Computer Vision

by T.F. Cootes, C.J. Taylor , 2000
"... ..."
Abstract - Cited by 357 (2 self) - Add to MetaCart
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Automatic construction of 3D statistical deformation models using non-rigid registration

by D. Rueckert, A. F. Frangi, J. A. Schnabel - IEEE Transactions on Medical Imaging , 2003
"... Abstract. In this paper we introduce the concept of statistical deformation models (SDM) which allow the construction of average models of the anatomy and their variability. SDMs are build by performing a statistical analysis of the deformations required to map anatomical features in one subject int ..."
Abstract - Cited by 161 (8 self) - Add to MetaCart
Abstract. In this paper we introduce the concept of statistical deformation models (SDM) which allow the construction of average models of the anatomy and their variability. SDMs are build by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to active shape models (ASM) which capture statistical information about shapes across a population but offers several new advantages: Firstly, SDMs can be constructed directly from images such as MR or CT without the need for segmentation which is usually a prerequisite for the construction of active shape models. Instead a non-rigid registration algorithm is used to compute the deformations required to establish correspondences between the reference subject and the subjects in the population class under investigation. Secondly, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3D nature of the underlying anatomy into account by analysing dense 3D deformation fields rather than only the 2D surface shape of anatomical structures. We demonstrate the applicability of this new framework to MR images of the brain and show results for the construction of anatomical models from 25 different subjects. 1
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...ers a powerful framework to facilitate the comparison of anatomy and function over time, between subjects, between groups of subjects and across sites. Consequently, a number of different elastic [1]–=-=[3]-=- and fluid [4], [5] warping techniques have been developed for this purpose. Recent reviews of different nonrigid registration techniques can be found in [6] and [7]. Traditional medical atlases conta...

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
"... ..."
Abstract - Cited by 159 (13 self) - Add to MetaCart
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Adaptive, Template Moderated, Spatially Varying Statistical Classification

by Simon K. Warfield, Michael Kaus, Ferenc A. Jolesz, Ron Kikinis - Medical Image Analysis , 1998
"... A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy. The new algorithm is a form of spatially varying classification (SVC), in which an explicit anatomical template is used to moderate the segmentation obtained by k Nearest Neigh ..."
Abstract - Cited by 113 (23 self) - Add to MetaCart
A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy. The new algorithm is a form of spatially varying classification (SVC), in which an explicit anatomical template is used to moderate the segmentation obtained by k Nearest Neighbour (\knnrule) statistical classification. The new algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which creates an adaptive, template moderated (ATM), spatially varying classification (SVC). The ATM SVC algorithm was applied to several segmentation problems, involving different types of imaging and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of babies, MRI of knee cartilage of normal volunteers) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumours, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone. \emph{Keywords:} template moderated segmentation, elastic matching, nearest neighbour classification, knee cartilage, neonate, brain, tumour
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...he nonlinear registration algorithm is not intrinsic to the ATM SVC algorithm, and it is possible to use other nonlinear registration algorithms (Bajcsy and Kova˘ci˘c, 1989; Christensen et al., 1994=-=; Thirion, 1998). -=-We have used the method of Dengler and Schmidt (1988) because it is sufficiently fast to be used routinely. The goal of the elastic matching algorithm is, ¦ given ¦ a source x¨ data set g1 and a ta...

Diffeomorphic Demons: Efficient Non-parametric Image Registration

by Tom Vercauteren , Xavier Pennec , Aymeric Perchant , Nicholas Ayache , 2008
"... We propose an efficient non-parametric 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 ..."
Abstract - Cited by 108 (13 self) - Add to MetaCart
We propose an efficient non-parametric 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.
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...007; Malis, 2004) provides a very interesting alternative to classical schemes. Looking at non-rigid image registration, one of the most efficient methods is the demons algorithm proposed by Thirion (=-=Thirion, 1998-=-). Several variants of the algorithm have been proposed depending on how the forces are computed. In (Wang et al., 2005; Rogelj and Kovačič, 2006) an ad hoc symmetrization of the demons forces similar...

Voxel-based morphometry using the ravens maps: Methods and validation using simulated longitudinal atrophy

by Christos Davatzikos, Ahmet Genc, Dongrong Xu, Susan M. Resnick - NeuroImage , 2001
"... Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in population-based 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 ..."
Abstract - Cited by 94 (30 self) - Add to MetaCart
Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in population-based 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, volume-preserving 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 voxel-based 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
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...deformable registration during the past several years (Gee et al., 1993; Christensen et al., 1994, 1997; Collins et al., 1994; Friston et al., 1995b; Thompson and Toga, 1996; Davatzikos, 1996a, 1997; =-=Thirion, 1998-=-; Freeborough and Fox, 1998; Chen et al., 1998; Wang and Staib, 1999). Somewhat related to these methods is landmark-based morphometry (Bookstein, 1989; Rangarajan and Duncan, 1998), although it is ba...

Volume-Preserving Nonrigid Registration of MR Breast Images Using Free-Form Deformation with an Incompressibility Constraint

by Torsten Rohlfing, Calvin R. Maurer Jr., David A. Bluemke, Michael A. Jacobs - IEEE Transactions on Medical Imaging , 2003
"... In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An infor ..."
Abstract - Cited by 81 (9 self) - Add to MetaCart
In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and postcontrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%--78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts ...

Symmetric log-domain diffeomorphic registration: A demons-based approach

by Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache - IMAG. COMPUT , 2008
"... Modern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of inverti ..."
Abstract - Cited by 76 (29 self) - Add to MetaCart
Modern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. In this work, we propose a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms. Our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. This implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. This also means that our output can be directly used for log-Euclidean statistics without relying on the heavy computation of the log of the spatial transformation. As it is often desirable, our algorithm is symmetric with respect to the order of the input images. Furthermore, we use an alternate optimization approach related to Thirion’s demons algorithm to provide a fast non-linear registration algorithm. First results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient.
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...d memory requirements. Recent work has strived towards bridging the gap between these rigorous mathematical tools and very efficient non-linear registration schemes such as Thirion’s demons algorithm =-=[8]-=-. On one hand it has been proposed to constrain the large deformation setting by using2 T. Vercauteren et al. transformations that satisfy the initial momentum conservation [9,10]. Similarly, in [1],...

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