1,130 research outputs found
Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
Accounting for 26% of all new cancer cases worldwide, breast cancer remains
the most common form of cancer in women. Although early breast cancer has a
favourable long-term prognosis, roughly a third of patients suffer from a
suboptimal aesthetic outcome despite breast conserving cancer treatment.
Clinical-quality 3D modelling of the breast surface therefore assumes an
increasingly important role in advancing treatment planning, prediction and
evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive
and either infrastructure-heavy or subject to motion artefacts. In this paper
we employ a single consumer-grade RGBD camera with an ICP-based registration
approach to jointly align all points from a sequence of depth images
non-rigidly. Subtle body deformation due to postural sway and respiration is
successfully mitigated leading to a higher geometric accuracy through
regularised locally affine transformations. We present results from 6 clinical
cases where our method compares well with the gold standard and outperforms a
previous approach. We show that our method produces better reconstructions
qualitatively by visual assessment and quantitatively by consistently obtaining
lower landmark error scores and yielding more accurate breast volume estimates
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
Local interpolation schemes for landmark-based image registration: a comparison
In this paper we focus, from a mathematical point of view, on properties and
performances of some local interpolation schemes for landmark-based image
registration. Precisely, we consider modified Shepard's interpolants,
Wendland's functions, and Lobachevsky splines. They are quite unlike each
other, but all of them are compactly supported and enjoy interesting
theoretical and computational properties. In particular, we point out some
unusual forms of the considered functions. Finally, detailed numerical
comparisons are given, considering also Gaussians and thin plate splines, which
are really globally supported but widely used in applications
Respecting Anatomically Rigid Regions in Nonrigid Registration
Medical image registration has received considerable attention in the medical imaging and computer vision communities because of the variety of ways in which it can potentially improve patient care. This application of image registration aligns images of regions of the human body for comparing images of the same patient at different times, for example, when assessing differences in a disease over time; comparing images of the same anatomical structure across different patients, for example, to understand patient variability; and comparing images of the same patient from different modalities that provide complementary information, for example, CT and PET to assess cancer.
The two primary types of registration make use of rigid and nonrigid transformations. Medical images typically contain some regions of bone, which behave rigidly, and some regions of soft tissue, which are able to deform. While a strictly rigid transformation would not account for soft tissue deformations, a strictly nonrigid transformation would not abide by the physical properties of the bone regions. Over the years, many image registration techniques have been developed and refined for particular applications but none of them compute a continuous transformation simultaneously containing both rigid and nonrigid regions.
This thesis focuses on using a sophisticated segmentation algorithm to identify and preserve bone structure in medical image registration while allowing the rest of the image to deform. The registration is performed by minimizing an objective function over a set of transformations that are defined in a piecewise manner: rigid over a portion of the domain, nonrigid over the remainder of the domain, and continuous everywhere. The objective function is minimized via steepest gradient descent, yielding an initial boundary value problem that is discretized in both time and space and solved numerically using multigrid. The registration results are compared to results of strictly rigid and nonrigid registrations
Symmetric and Transitive Registration of Image Sequences
This paper presents a method for constructing symmetric and transitive algorithms for registration of image sequences from
image registration algorithms that do not have these two properties. The method is applicable to both rigid and nonrigid registration
and it can be used with linear or periodic image sequences. The symmetry and transitivity properties are satisfied exactly (up to
the machine precision), that is, they always hold regardless of the image type, quality, and the registration algorithm as long as
the computed transformations are invertable. These two properties are especially important in motion tracking applications since
physically incorrect deformations might be obtained if the registration algorithm is not symmetric and transitive. The method was tested on two sequences of cardiac magnetic resonance images using two different nonrigid image registration
algorithms. It was demonstrated that the transitivity and symmetry errors of the symmetric and transitive modification of the
algorithms could be made arbitrary small when the computed transformations are invertable, whereas the corresponding errors
for the nonmodified algorithms were on the order of the pixel size. Furthermore, the symmetric and transitive modification of the
algorithms had higher registration accuracy than the nonmodified algorithms for both image sequences
A Simple Regularizer for B-spline Nonrigid Image Registration That Encourages Local Invertibility
Nonrigid image registration is an important task for many medical imaging applications. In particular, for radiation oncology it is desirable to track respiratory motion for thoracic cancer treatment. B-splines are convenient for modeling nonrigid deformations, but ensuring invertibility can be a challenge. This paper describes sufficient conditions for local invertibility of deformations based on B-spline bases. These sufficient conditions can be used with constrained optimization to enforce local invertibility. We also incorporate these conditions into nonrigid image registration methods based on a simple penalty approach that encourages diffeomorphic deformations. Traditional Jacobian penalty methods penalize negative Jacobian determinant values only at grid points. In contrast, our new method enforces a sufficient condition for invertibility directly on the deformation coefficients to encourage invertibility globally over a 3-D continuous domain. The proposed penalty approach requires substantially less compute time than Jacobian penalties per iteration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85951/1/Fessler21.pd
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