120 research outputs found
Prostate Biopsy Assistance System with Gland Deformation Estimation for Enhanced Precision
Computer-assisted prostate biopsies became a very active research area during
the last years. Prostate tracking makes it possi- ble to overcome several
drawbacks of the current standard transrectal ultrasound (TRUS) biopsy
procedure, namely the insufficient targeting accuracy which may lead to a
biopsy distribution of poor quality, the very approximate knowledge about the
actual location of the sampled tissues which makes it difficult to implement
focal therapy strategies based on biopsy results, and finally the difficulty to
precisely reach non-ultrasound (US) targets stemming from different modalities,
statistical atlases or previous biopsy series. The prostate tracking systems
presented so far are limited to rigid transformation tracking. However, the
gland can get considerably deformed during the intervention because of US probe
pres- sure and patient movements. We propose to use 3D US combined with
image-based elastic registration to estimate these deformations. A fast elastic
registration algorithm that copes with the frequently occurring US shadows is
presented. A patient cohort study was performed, which yielded a statistically
significant in-vivo accuracy of 0.83+-0.54mm.Comment: This version of the paper integrates a correction concerning the
local similarity measure w.r.t. the proceedings (this typing error could not
be corrected before editing the proceedings
Differences in the effects of rouding errors in Krylov solvers for symmetric indefinite linear systems
The threeterm Lanczos process for a symmetric matrix leads to bases for Krylov
subspaces of increasing dimension. The Lanczos basis, together with the recurrence coe#cients,
can be used for the solution of symmetric indefinite linear systems, by solving a reduced system
in one way or another. This leads to wellknown methods: MINRES (minimal residual), GMRES
(generalized minimal residual), and SYMMLQ (symmetric LQ). We will discuss in what way and to
what extent these approaches di#er in their sensitivity to rounding errors.
In our analysis we will assume that the Lanczos basis is generated in exactly the same way for
the di#erent methods, and we will not consider the errors in the Lanczos process itself. We will show
that the method of solution may lead, under certain circumstances, to large additional errors, which
are not corrected by continuing the iteration process.
Our findings are supported and illustrated by numerical examples
Fast Predictive Image Registration
We present a method to predict image deformations based on patch-wise image
appearance. Specifically, we design a patch-based deep encoder-decoder network
which learns the pixel/voxel-wise mapping between image appearance and
registration parameters. Our approach can predict general deformation
parameterizations, however, we focus on the large deformation diffeomorphic
metric mapping (LDDMM) registration model. By predicting the LDDMM
momentum-parameterization we retain the desirable theoretical properties of
LDDMM, while reducing computation time by orders of magnitude: combined with
patch pruning, we achieve a 1500x/66x speed up compared to GPU-based
optimization for 2D/3D image registration. Our approach has better prediction
accuracy than predicting deformation or velocity fields and results in
diffeomorphic transformations. Additionally, we create a Bayesian probabilistic
version of our network, which allows evaluation of deformation field
uncertainty through Monte Carlo sampling using dropout at test time. We show
that deformation uncertainty highlights areas of ambiguous deformations. We
test our method on the OASIS brain image dataset in 2D and 3D
An improved model for joint segmentation and registration based on linear curvature smoother
Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope. </jats:p
Computing Topology Preservation of RBF Transformations for Landmark-Based Image Registration
In image registration, a proper transformation should be topology preserving.
Especially for landmark-based image registration, if the displacement of one
landmark is larger enough than those of neighbourhood landmarks, topology
violation will be occurred. This paper aim to analyse the topology preservation
of some Radial Basis Functions (RBFs) which are used to model deformations in
image registration. Mat\'{e}rn functions are quite common in the statistic
literature (see, e.g. \cite{Matern86,Stein99}). In this paper, we use them to
solve the landmark-based image registration problem. We present the topology
preservation properties of RBFs in one landmark and four landmarks model
respectively. Numerical results of three kinds of Mat\'{e}rn transformations
are compared with results of Gaussian, Wendland's, and Wu's functions
Patch-based nonlinear image registration for gigapixel whole slide images
Producción CientíficaImage registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level
A variational joint segmentation and registration framework for multimodal images
Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results
Variational Registration of Multiple Images with the SVD based SqN Distance Measure
Image registration, especially the quantification of image similarity, is an
important task in image processing. Various approaches for the comparison of
two images are discussed in the literature. However, although most of these
approaches perform very well in a two image scenario, an extension to a
multiple images scenario deserves attention. In this article, we discuss and
compare registration methods for multiple images. Our key assumption is, that
information about the singular values of a feature matrix of images can be used
for alignment. We introduce, discuss and relate three recent approaches from
the literature: the Schatten q-norm based SqN distance measure, a rank based
approach, and a feature volume based approach. We also present results for
typical applications such as dynamic image sequences or stacks of histological
sections. Our results indicate that the SqN approach is in fact a suitable
distance measure for image registration. Moreover, our examples also indicate
that the results obtained by SqN are superior to those obtained by its
competitors.Comment: 12 pages, 5 figures, accepted at the conference "Scale Space and
Variational Methods" in Hofgeismar, Germany 201
Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation
This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM). but with a more efficient optimisation scheme - both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data. (C) 2011 Elsevier Inc. All rights reserved
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