| 1. |
- Aanæs, Henrik, et al.
(författare)
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Camera Resectioning from a Box
- 2009
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Ingår i: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349.
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Konferensbidrag (refereegranskat)abstract
- In this paper we describe how we can do camera resectioning from a box with unknown dimensions, i.e. determine the camera model, assuming that image pixels are square. This assumption is equivalent to assuming that the camera as an aspect ratio of one and zero skew, and holds for most - if not all - digital cameras. Our proposed method works by first deriving 9 linear constraints on the projective camera matrix from the box, leaving a 3 dimensional subspace in which the projective camera matrix can lye. A single solution in this 3D subspace is then found via a method by Triggs in 1999, which uses the squared pixel assumption to set up a 4th degree polynomial to which the solution is the desired model. This approach is, however, numerically challenging, and we use several means to combat this issue. Lastly the solution is refined in an iterative manner, i.e. using bundle adjustment.
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| 2. |
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| 3. |
- Agarwal, S, et al.
(författare)
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Practical global optimization for multiview geometry
- 2006
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Ingår i: Lecture Notes in Computer Science. - 1611-3349. ; 3951:Pt 1: Proceedings, s. 592-605
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and hemography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L-2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L-1-norm which is less sensitive to outliers. The efficacy of our algorithm is empirically demonstrated by good performance on experiments for both synthetic and real data. An open source MATLAB toolbox that implements the algorithm is also made available to facilitate further research.
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| 4. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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Optimal Experiment Design for Mono-Exponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging
- 2015
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Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2015
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Tidskriftsartikel (refereegranskat)abstract
- The mono-exponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics.The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for mono-exponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (?-optimal design). In contrast to previous methods, ?-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, range of ?-values). Using Monte Carlo simulations we show that the ?-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.
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| 5. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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Optimal Gradient Encoding Schemes for Diffusion Tensor and Kurtosis Imaging
- 2016
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Ingår i: IEEE transactions on Computational Imaging. - 2333-9403. ; 2:3, s. 375 - 391
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion-derived parameters find application in characterizing pathological and developmental changes in living tissues. Robust estimation of these parameters is important because they are used for medical diagnosis. An optimal gradient encoding scheme (GES) is one that minimizes the variance of the estimated diffusion parameters. This paper proposes a method for optimal GES design for two diffusion models: high-order diffusion tensor (HODT) imaging and diffusion kurtosis imaging (DKI). In both cases, the optimal GES design problem is formulated as a D-optimal (minimum determinant) experiment design problem. Then, using convex relaxation, it is reformulated as a semidefinite programming problem. Solving these problems we show that: 1) there exists a D-optimal solution for DKI that is simultaneously D-optimal for second- and fourth-order diffusion tensor imaging (DTI); 2) the traditionally used icosahedral scheme is approximately D-optimal for DTI and DKI; 3) the proposed D-optimal design is rotation invariant; 4) the proposed method can be used to compute the optimal design ($b$ -values and directions) for an arbitrary number of measurements and shells; and 5) using the proposed method one can obtain uniform distribution of gradient encoding directions for a typical number of measurements. Importantly, these theoretical findings provide the first mathematical proof of the optimality of uniformly distributed GESs for DKI and HODT imaging. The utility of the proposed method is further supported by the evaluation results and comparisons with with existing methods.
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| 6. |
- Alvén, Jennifer, 1989, et al.
(författare)
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A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
- 2019
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Ingår i: Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030322441 - 9783030322458 ; 11765 LNCS, s. 355-363
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Konferensbidrag (refereegranskat)abstract
- The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results.
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| 7. |
- Alvén, Jennifer, 1989, et al.
(författare)
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Shape-aware label fusion for multi-atlas frameworks
- 2019
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 124, s. 109-117
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Tidskriftsartikel (refereegranskat)abstract
- Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures.
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| 8. |
- Alvén, Jennifer, 1989, et al.
(författare)
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Shape-aware multi-atlas segmentation
- 2016
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. ; 0, s. 1101-1106
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Konferensbidrag (refereegranskat)abstract
- Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.
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| 9. |
- Alvén, Jennifer, 1989, et al.
(författare)
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Überatlas: Fast and robust registration for multi-atlas segmentation
- 2016
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 80, s. 249-255
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Tidskriftsartikel (refereegranskat)abstract
- Multi-atlas segmentation has become a frequently used tool for medical image segmentation due to its outstanding performance. A computational bottleneck is that all atlas images need to be registered to a new target image. In this paper, we propose an intermediate representation of the whole atlas set – an überatlas – that can be used to speed up the registration process. The representation consists of feature points that are similar and detected consistently throughout the atlas set. A novel feature-based registration method is presented which uses the überatlas to simultaneously and robustly find correspondences and affine transformations to all atlas images. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding ground truth. Our approach succeeds in producing better and more robust segmentation results compared to three baseline methods, two intensity-based and one feature-based, and significantly reduces the running times.
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| 10. |
- Alvén, Jennifer, 1989, et al.
(författare)
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Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
- 2015
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319196640 ; 9127, s. 92-102
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Konferensbidrag (refereegranskat)abstract
- Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensitybased registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based registration method for affine registration is presented. The algorithm constructs an abstract representation of the entire atlas set, an uberatlas, through clustering of features that are similar and detected consistently through the atlas set. This is done offline. At runtime only the feature clusters are matched to the target image, simultaneously yielding robust correspondences to all atlases in the atlas set from which the affine transformations can be estimated efficiently. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding gold standards. Our approach succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature-based, and significantly reduces the running times.
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