18,399 research outputs found
Symmetry-guided nonrigid registration: the case for distortion correction in multidimensional photoemission spectroscopy
Image symmetrization is an effective strategy to correct symmetry distortion
in experimental data for which symmetry is essential in the subsequent
analysis. In the process, a coordinate transform, the symmetrization transform,
is required to undo the distortion. The transform may be determined by image
registration (i.e. alignment) with symmetry constraints imposed in the
registration target and in the iterative parameter tuning, which we call
symmetry-guided registration. An example use case of image symmetrization is
found in electronic band structure mapping by multidimensional photoemission
spectroscopy, which employs a 3D time-of-flight detector to measure electrons
sorted into the momentum (, ) and energy () coordinates. In
reality, imperfect instrument design, sample geometry and experimental settings
cause distortion of the photoelectron trajectories and, therefore, the symmetry
in the measured band structure, which hinders the full understanding and use of
the volumetric datasets. We demonstrate that symmetry-guided registration can
correct the symmetry distortion in the momentum-resolved photoemission
patterns. Using proposed symmetry metrics, we show quantitatively that the
iterative approach to symmetrization outperforms its non-iterative counterpart
in the restored symmetry of the outcome while preserving the average shape of
the photoemission pattern. Our approach is generalizable to distortion
corrections in different types of symmetries and should also find applications
in other experimental methods that produce images with similar features
Finite element surface registration incorporating curvature, volume preservation, and statistical model information
We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models
Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images
We present an information-theoretic approach to the registration of images
with directional information, and especially for diffusion-Weighted Images
(DWI), with explicit optimization over the directional scale. We call it
Locally Orderless Registration with Directions (LORD). We focus on normalized
mutual information as a robust information-theoretic similarity measure for
DWI. The framework is an extension of the LOR-DWI density-based hierarchical
scale-space model that varies and optimizes the integration, spatial,
directional, and intensity scales. As affine transformations are insufficient
for inter-subject registration, we extend the model to non-rigid deformations.
We illustrate that the proposed model deforms orientation distribution
functions (ODFs) correctly and is capable of handling the classic complex
challenges in DWI-registrations, such as the registration of fiber-crossings
along with kissing, fanning, and interleaving fibers. Our experimental results
clearly illustrate a novel promising regularizing effect, that comes from the
nonlinear orientation-based cost function. We show the properties of the
different image scales and, we show that including orientational information in
our model makes the model better at retrieving deformations in contrast to
standard scalar-based registration.Comment: 16 pages, 19 figure
A Scale-Space Medialness Transform Based on Boundary Concordance Voting
The Concordance-based Medial Axis Transform (CMAT) presented in this paper is a multiscale medial axis (MMA) algorithm that computes the medial response from grey-level boundary measures. This non-linear operator responds only to symmetric structures, overcoming the limitations of linear medial operators which create âside-lobeâ responses for symmetric structures and respond to edge structures. In addition, the spatial localisation of the medial axis and the identification of object width is improved in the CMAT algorithm compared with linear algorithms. The robustness of linear medial operators to noise is preserved in our algorithm. The effectiveness of the CMAT is accredited to the concordance property described in this paper. We demonstrate the performance of this method with test figures used by other authors and medical images that are relatively complex in structure. In these complex images the benefit of the improved response of our non-linear operator is clearly visible
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