Results 1 - 10 of 209

Non-parametric Diffeomorphic Image Registration with Demons Algorithm

by Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache , 2007
"... We propose a non-parametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphi ..."
Abstract - Cited by 48 (9 self) - Add to MetaCart
We propose a non-parametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure 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 free form deformations 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 true ones in terms of Jacobians.
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Grid Powered Nonlinear Image Registration with Locally Adaptive Regularization

by Radu Stefanescu, Xavier Pennec, Nicholas Ayache - MICCAI 2003 Special Issue , 2004
"... Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not su#cient. In this paper, we present an algorithm that uses a ..."
Abstract - Cited by 36 (11 self) - Add to MetaCart
Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not su#cient. In this paper, we present an algorithm that uses a priori information about the nature of imaged objects in order to adapt the regularization of the deformations. We also present a robustness improvement that gives higher weight to those points in images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. In order to improve the usability of the parallel software by a clinical user, we have implemented it as a grid service that can be controlled by a graphics workstation embedded in the clinical environment. Results on inter-subject pairs of images show that our method can take into account the large variability of most brain structures. The registration time for images 124 is 5 minutes on 15 standard PCs. A comparison of our non-stationary visco-elastic smoothing versus solely elastic or fluid regularizations shows that our algorithm converges faster towards a more optimal solution in terms of accuracy and transformation regularity.

Deformable medical image registration: A survey

by Aristeidis Sotiras, Christos Davatzikos, Nikos Paragios - IEEE TRANSACTIONS ON MEDICAL IMAGING , 2013
"... Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
Abstract - Cited by 35 (1 self) - Add to MetaCart
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
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Intensity gradient based registration and fusion of multi-modal images

by Eldad Haber, Jan Modersitzki - Methods of Information in Medicine, Schattauer Verlag , 2006
"... multi-modal images ..."
Abstract - Cited by 34 (4 self) - Add to MetaCart
multi-modal images
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Adaptive stochastic gradient descent optimisation for image registration.

by S Klein , J P W Pluim , M Staring , M A Viergever - International Journal of Computer Vision , 2009
"... Abstract We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz (J. Math. Sci. 120(1): [964][965][966][967][968][969][970][971][972][973] 2004). Our main methodological c ..."
Abstract - Cited by 30 (4 self) - Add to MetaCart
Abstract We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz (J. Math. Sci. 120(1): [964][965][966][967][968][969][970][971][972][973] 2004). Our main methodological contribution is the derivation of an image-driven mechanism to select proper values for the most important free parameters of the method. The selection mechanism employs general characteristics of the cost functions that commonly occur in intensity-based image registration. Also, the theoretical convergence conditions of the optimisation method are taken into account. The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive RobbinsMonro (RM) algorithm. Both ASGD and RM employ a stochastic subsampling technique to accelerate the optimisation process. Registration experiments were performed on 3D CT and MR data of the head, lungs, and prostate, using various similarity measures and transformation models. The results indicate that ASGD is robust to these variations in the registration framework and is less sensitive to the settings of the user-defined parameters than RM. The main disadvantage of RM is the need for a predetermined step size function. The ASGD method provides a solution for that issue.
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Image Compression with Anisotropic Diffusion

by Irena Galić, Joachim Weickert, Martin Welk, Andrés Bruhn, Alexander Belyaev, Hans-Peter Seidel , 2008
"... Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel framework for image compression that makes us ..."
Abstract - Cited by 24 (15 self) - Add to MetaCart
Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel framework for image compression that makes use of the interpolation qualities of edge-enhancing diffusion. Although this anisotropic diffusion equation with a diffusion tensor was originally proposed for image denoising, we show that it outperforms many other PDEs when sparse scattered data must be interpolated. To exploit this property for image compression, we consider an adaptive triangulation method for removing less significant pixels from the image. The remaining points serve as scattered interpolation data for the diffusion process. They can be coded in

Numerical methods for volume preserving image registration

by Eldad Haber, Jan Modersitzki - Inverse Problems, Institute of Physics Publishing , 2004
"... Image registration techniques are used routinely in a variety of today’s medical imaging diagnosis. Since the problem is ill-posed, one may like to add additional information about distortions. This applies, for example, to the registration of contrast enhanced images, where variations of substructu ..."
Abstract - Cited by 22 (7 self) - Add to MetaCart
Image registration techniques are used routinely in a variety of today’s medical imaging diagnosis. Since the problem is ill-posed, one may like to add additional information about distortions. This applies, for example, to the registration of contrast enhanced images, where variations of substructures are not related to patient motion but to contrast uptake. Here, one may only be interested in registrations which do not alter the volume of any substructure. In this paper we discuss image registration techniques with a focus on volume preserving constraints. These constraints can reduce the non-uniqueness of the registration problem significantly. Our implementation is based on a constrained optimization formulation. Upon discretization, we obtain a large, discrete, highly nonlinear optimization problem and the necessary conditions for the solution form a discretize nonlinear partial differential equation. To solve the problem we use a variant of Sequential Quadratic Programming method. Moreover, we present results on synthetic as well as on real life data. 1
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Inter-subject alignment of human cortical anatomy using functional connectivity.

by Bryan R Conroy , Benjamin D Singer , J Swaroop Guntupalli , Peter J Ramadge , James V Haxby - NeuroImage, , 2013
"... Inter-subject alignment of functional MRI (fMRI) data is necessary for group analyses. The standard approach to this problem matches anatomical features of the brain, such as major anatomical landmarks or cortical curvature. Precise alignment of functional cortical topographies, however, cannot be ..."
Abstract - Cited by 22 (0 self) - Add to MetaCart
Inter-subject alignment of functional MRI (fMRI) data is necessary for group analyses. The standard approach to this problem matches anatomical features of the brain, such as major anatomical landmarks or cortical curvature. Precise alignment of functional cortical topographies, however, cannot be derived using only anatomical features. We propose a new inter-subject registration algorithm that aligns intra-subject patterns of functional connectivity across subjects. We derive functional connectivity patterns by correlating fMRI BOLD time-series, measured during movie viewing, between spatially remote cortical regions. We validate our technique extensively on real fMRI experimental data and compare our method to two state-of-the-art inter-subject registration algorithms. By cross-validating our method on independent datasets, we show that the derived alignment generalizes well to other experimental paradigms.

Mumford–Shah Model for One-to-One Edge Matching

by Jingfeng Han, Benjamin Berkels, Marc Droske, Joachim Hornegger, Martin Rumpf, Carlo Schaller, Jasmin Scorzin, Horst Urbach
"... 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 ..."
Abstract - Cited by 21 (2 self) - Add to MetaCart
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 one-to-one 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 minimization-type 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.
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Insight into efficient image registration techniques and the demons algorithm

by Tom Vercauteren, Xavier Pennec, Ezio Malis, Aymeric Perchant, Nicholas Ayache - IN: PROC. IPMI’07 , 2007
"... As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is cons ..."
Abstract - Cited by 21 (6 self) - Add to MetaCart
As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is considered as almost solved for linear registration, we show in this paper that some tools that have recently been developed in the field of vision-based robot control can outperform classical solutions. The adequacy of these tools for linear image registration leads us to revisit non-linear registration and allows us to provide interesting theoretical roots to the different variants of Thirion’s demons algorithm. This analysis predicts a theoretical advantage to the symmetric forces variant of the demons algorithm. We show that, on controlled experiments, this advantage is confirmed, and yields a faster convergence.
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