219 research outputs found
An efficient stochastic approach to groupwise non-rigid image registration
The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, in- cluding medical imaging, automatic construction of statis- tical models of appearance and analysis of facial dynam- ics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of ex- amples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed suffi- ciently in the groupwise registration literature. In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel di- mensionality reduction techniques specific to the problem of groupwise image registration and from comparative insen- sitivity of the adopted optimisation scheme (Simultaneous Perturbation Stochastic Approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algo- rithm is formulated in way readily suited to implementation on graphics processing units (GPU). In evaluation of our method we show a high robust- ness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter- and intra-person variation, and show consid- erable improvement in terms of accuracy of solution and speed compared to traditional methods
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of
modalities (MRI, CT, PET, etc.), to highlight different structures or
pathologies. As patient movement between scans or scanning session is
unavoidable, registration is often an essential step before any subsequent
image analysis. In this paper, we introduce a cost function based on joint
total variation for such multimodal image registration. This cost function has
the advantage of enabling principled, groupwise alignment of multiple images,
whilst being insensitive to strong intensity non-uniformities. We evaluate our
algorithm on rigidly aligning both simulated and real 3D brain scans. This
validation shows robustness to strong intensity non-uniformities and low
registration errors for CT/PET to MRI alignment. Our implementation is publicly
available at https://github.com/brudfors/coregistration-njtv
Fast 4D elastic group-wise image registration. Convolutional interpolation revisited
Background and Objective:This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. Methods:Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. Results:The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation. Conclusions:Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.MEC-TEC2017-82408-
Intrasubject multimodal groupwise registration with the conditional template entropy
Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the
statistical dependency and finding the anatomical correspondences among an
arbitrary number of medical images. The method builds on a novel formulation of
the -dimensional joint intensity distribution by representing the common
anatomy as latent variables and estimating the appearance model with
nonparametric estimators. Through connection to maximum likelihood and the
expectation-maximization algorithm, an information\hyp{}theoretic metric called
-metric and a co-registration algorithm named -CoReg
are induced, allowing groupwise registration of the observed images with
computational complexity of . Moreover, the method naturally
extends for a weakly-supervised scenario where anatomical labels of certain
images are provided. This leads to a combined\hyp{}computing framework
implemented with deep learning, which performs registration and segmentation
simultaneously and collaboratively in an end-to-end fashion. Extensive
experiments were conducted to demonstrate the versatility and applicability of
our model, including multimodal groupwise registration, motion correction for
dynamic contrast enhanced magnetic resonance images, and deep combined
computing for multimodal medical images. Results show the superiority of our
method in various applications in terms of both accuracy and efficiency,
highlighting the advantage of the proposed representation of the imaging
process
Groupwise Multimodal Image Registration Using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv
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