4,931 research outputs found
Affine Registration of label maps in Label Space
Two key aspects of coupled multi-object shape\ud
analysis and atlas generation are the choice of representation\ud
and subsequent registration methods used to align the sample\ud
set. For example, a typical brain image can be labeled into\ud
three structures: grey matter, white matter and cerebrospinal\ud
fluid. Many manipulations such as interpolation, transformation,\ud
smoothing, or registration need to be performed on these images\ud
before they can be used in further analysis. Current techniques\ud
for such analysis tend to trade off performance between the two\ud
tasks, performing well for one task but developing problems when\ud
used for the other.\ud
This article proposes to use a representation that is both\ud
flexible and well suited for both tasks. We propose to map object\ud
labels to vertices of a regular simplex, e.g. the unit interval for\ud
two labels, a triangle for three labels, a tetrahedron for four\ud
labels, etc. This representation, which is routinely used in fuzzy\ud
classification, is ideally suited for representing and registering\ud
multiple shapes. On closer examination, this representation\ud
reveals several desirable properties: algebraic operations may\ud
be done directly, label uncertainty is expressed as a weighted\ud
mixture of labels (probabilistic interpretation), interpolation is\ud
unbiased toward any label or the background, and registration\ud
may be performed directly.\ud
We demonstrate these properties by using label space in a gradient\ud
descent based registration scheme to obtain a probabilistic\ud
atlas. While straightforward, this iterative method is very slow,\ud
could get stuck in local minima, and depends heavily on the initial\ud
conditions. To address these issues, two fast methods are proposed\ud
which serve as coarse registration schemes following which the\ud
iterative descent method can be used to refine the results. Further,\ud
we derive an analytical formulation for direct computation of the\ud
"group mean" from the parameters of pairwise registration of all\ud
the images in the sample set. We show results on richly labeled\ud
2D and 3D data sets
Parameter selection in sparsity-driven SAR imaging
We consider a recently developed sparsity-driven synthetic aperture radar (SAR) imaging approach which can produce superresolution, feature-enhanced images. However, this regularization-based approach requires the selection of a hyper-parameter in order to generate such high-quality images. In this paper we present a number of techniques for automatically selecting the hyper-parameter
involved in this problem. In particular, we propose and develop numerical procedures for the use of Stein’s unbiased risk estimation, generalized cross-validation, and L-curve techniques for automatic parameter choice. We demonstrate and compare the effectiveness of these procedures through experiments based on both simple synthetic scenes, as well as electromagnetically simulated realistic data. Our results suggest that sparsity-driven SAR imaging coupled with the proposed automatic parameter choice procedures offers significant improvements over conventional SAR imaging
Distributed-memory large deformation diffeomorphic 3D image registration
We present a parallel distributed-memory algorithm for large deformation
diffeomorphic registration of volumetric images that produces large isochoric
deformations (locally volume preserving). Image registration is a key
technology in medical image analysis. Our algorithm uses a partial differential
equation constrained optimal control formulation. Finding the optimal
deformation map requires the solution of a highly nonlinear problem that
involves pseudo-differential operators, biharmonic operators, and pure
advection operators both forward and back- ward in time. A key issue is the
time to solution, which poses the demand for efficient optimization methods as
well as an effective utilization of high performance computing resources. To
address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov
solver. Our algorithm integrates several components: a spectral discretization
in space, a semi-Lagrangian formulation in time, analytic adjoints, different
regularization functionals (including volume-preserving ones), a spectral
preconditioner, a highly optimized distributed Fast Fourier Transform, and a
cubic interpolation scheme for the semi-Lagrangian time-stepping. We
demonstrate the scalability of our algorithm on images with resolution of up to
on the "Maverick" and "Stampede" systems at the Texas Advanced
Computing Center (TACC). The critical problem in the medical imaging
application domain is strong scaling, that is, solving registration problems of
a moderate size of ---a typical resolution for medical images. We are
able to solve the registration problem for images of this size in less than
five seconds on 64 x86 nodes of TACC's "Maverick" system.Comment: accepted for publication at SC16 in Salt Lake City, Utah, USA;
November 201
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
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