| 4641. |
- Öfverstedt, Johan, et al.
(författare)
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Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
- 2022
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Konferensbidrag (refereegranskat)abstract
- Multimodal image alignment involves finding spatial correspondences between volumes varying in appearance and structure. Automated alignment methods are often based on local optimization that can be highly sensitive to initialization. We propose a novel efficient algorithm for computing similarity of normalized gradient fields (NGF) in the frequency domain, which we globally optimize to achieve rigid multimodal 3D image alignment. We validate the method experimentally on a dataset comprised of 20 brain volumes acquired in four modalities (T1w, Flair, CT, [18F] FDG PET), synthetically displaced with known transformations. The proposed method exhibits excellent performance on all six possible modality combinations and outperforms the four considered reference methods by a large margin. An important advantage of the method is its speed; global rigid alignment of 3.4 Mvoxel volumes requires approximately 40 seconds of computation, and the proposed algorithm outperforms a direct algorithm for the same task by more than three orders of magnitude. Open-source code is provided.
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| 4642. |
- Öfverstedt, Johan, et al.
(författare)
-
Efficient Algorithms for Global Multimodal Image Registration
- 2022
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Multimodal image registration is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. Two similarity measures widely used in multimodal image registration are mutual information (MI) and similarity of normalized gradient fields (NGF). We propose efficient algorithms for computing MI and similarity of NGF for all discrete axis-aligned shifts in the frequency domain. These fast algorithms enable highly reliable global registration of multimodal images, also for very large displacements, which we confirm by their performance evaluation on a number of different pairs of modalities.We consider four datasets, and observe that global maximization of MI is the best choice for two datasets/applications in 2D, while global maximization of similarity of NGF performs best on the remaining two datasets, of which one consists of 2D images, and the other consists of 3D data. This confirms the relevance of both methods; their properties recommend them for application in different scenarios.
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| 4643. |
- Öfverstedt, Johan, et al.
(författare)
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Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information
- 2019
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Ingår i: IEEE Transactions on Image Processing. - : IEEE. - 1057-7149 .- 1941-0042. ; 28:7, s. 3584-3597
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Tidskriftsartikel (refereegranskat)abstract
- Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.
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| 4644. |
- Öfverstedt, Johan, et al.
(författare)
-
Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.
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| 4645. |
- Öfverstedt, Johan, et al.
(författare)
-
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment
- 2022
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 159, s. 196-203
-
Tidskriftsartikel (refereegranskat)abstract
- Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. The information-theoretic concept of mutual information (MI) is widely used as a similarity measure to guide multimodal alignment processes, where most works have focused on local maximization of MI, which typically works well only for small displacements. This points to a need for global maximization of MI, which has previously been computationally infeasible due to the high run-time complexity of existing algorithms. We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain. We show that the algorithm is equivalent to a direct method while superior in terms of run-time. Furthermore, we propose a method for multimodal image alignment for transformation models with few degrees of freedom (e.g., rigid) based on the proposed CMIF-algorithm. We evaluate the efficacy of the proposed method on three distinct benchmark datasets, containing remote sensing images, cytological images, and histological images, and we observe excellent success-rates (in recovering known rigid transformations), overall outperforming alternative methods, including local optimization of MI, as well as several recent deep learning-based approaches. We also evaluate the run-times of a GPU implementation of the proposed algorithm and observe speed-ups from 100 to more than 10,000 times for realistic image sizes compared to a GPU implementation of a direct method. Code is shared as open-source at github.com/MIDA-group/globalign.
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| 4646. |
- Öfverstedt, Johan, et al.
(författare)
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Fast Computation of Mutual Information with Application to Global Multimodal Image Alignment of Micrographs
- 2021
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques, to facilitate heterogeneous data fusion and correlative analysis. Image alignment methods based on maximization of mutual information (MI) are well established and a part of most general-purpose multimodal image alignment packages. MI maximization is typically used in local optimization frameworks where an initial guess for the transformation parameters is required, and where the local region around this guess is explored, guided by the derivatives of MI. Local optimization often implies substantial robustness and usability challenges: (i) a good initial guess can be hard to find, (ii) the optimizer may fail to converge to the sought solution, and (iii) there tend to be many hyper-parameters to tune. These three challenges are likely to be present when applying MI to multimodal microscopy scenarios, due to sparseness of key structures, indistinct local features, and large displacements.We recently proposed a novel algorithm for computing MI between two images for all discrete displacements. This algorithm, based on cross-correlation computed in the frequency domain, is substantially more efficient than existing algorithms – it is several orders of magnitude faster. Applying the algorithm for a suitable set of rotation angles, we obtain a global optimization method for rigid multimodal image alignment that successfully addresses the three previously listed challenges of local maximization of MI.To evaluate the efficacy of the proposed method, we selected public benchmark datasets comprising aligned multimodal cytological image pairs (fluorescence and quantitative phase imaging (QPI)), and aligned multimodal histological image pairs (brightfield (BF) and second-harmonic generation (SHG) imaging), where each image was subjected to synthetic rigid transformations. We observed excellent performance, in terms of the rate of successful recovery of the known transformation, on both datasets, outperforming a number of existing methods with a wide margin, including local maximization of MI as well as recent deep learning-based approaches.We implemented the method in PyTorch to enable the use of GPU acceleration to speed up the runtime and facilitate practical applicability. The implementation and reference to our full study is available at github.com/MIDA-group/globalign.
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| 4647. |
- Öfverstedt, Johan, et al.
(författare)
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INSPIRE : Intensity and Spatial Information-Based Deformable Image Registration
- 2023
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 18:3
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Tidskriftsartikel (refereegranskat)abstract
- We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into an elastic B-splines-based transformation model and incorporates an inverse inconsistency penalization supporting symmetric registration performance. We introduce several theoretical and algorithmic solutions which provide high computational efficiency and thereby applicability of the proposed framework in a wide range of real scenarios. We show that INSPIRE delivers highly accurate, as well as stable and robust registration results. We evaluate the method on a 2D dataset created from retinal images, characterized by presence of networks of thin structures. Here INSPIRE exhibits excellent performance, substantially outperforming the widely used reference methods. We also evaluate INSPIRE on the Fundus Image Registration Dataset (FIRE), which consists of 134 pairs of separately acquired retinal images. INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods. We also evaluate the method on four benchmark datasets of 3D magnetic resonance images of brains, for a total of 2088 pairwise registrations. A comparison with 17 other state-of-the-art methods reveals that INSPIRE provides the best overall performance. Code is available at github.com/MIDA-group/inspire
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| 4648. |
- Öfverstedt, Johan, 1985-
(författare)
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Methods for Reliable Image Registration : Algorithms, Distance Measures, and Representations
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Much biomedical and medical research relies on the collection of ever-larger amounts of image data (both 2D images and 3D volumes, as well as time-series) and increasingly from multiple sources. Image registration, the process of finding correspondences between images based on the affinity of features of interest, is often required as a vital step towards the final analysis, which may consist of a comparison of images, measurement of movement, or fusion of complementary information. The contributions in this work are centered around reliable image registration methods for both 2D and 3D images with the aim of wide applicability: similarity and distance measures between images for image registration, algorithms for efficient computation of these, and other commonly used measures for both local and global optimization frameworks, and representations for multimodal image registration where the appearance and structures present in the images may vary dramatically.The main contributions are: (i) distance measures for affine symmetric intensity image registration, combining intensity and spatial information based on the notion of alpha-cuts from fuzzy set theory; (ii) the extension of the affine registration method to more flexible deformable transformation models, leading to the framework Intensity and Spatial Information-Based Deformable Image Registration (INSPIRE); (iii) two efficient algorithms for computing the proposed distances and their spatial gradients and thereby enabling local gradient-based optimization; (iv) a contrastive representation learning method, Contrastive Multimodal Image Representation for Registration (CoMIR), utilizing deep learning techniques to obtain common representations that can be registered using methods designed for monomodal scenarios; (v) efficient algorithms for global optimization of mutual information and similarities of normalized gradient fields; (vi) a comparative study exploring the applicability of modern image-to-image translation methods to facilitate multimodal registration; (vii) the Stochastic Distance Transform, using the theory of discrete random sets to offer improved noise-insensitivity to distance computations; (viii) extensive evaluation of the proposed image registration methods on a number of different datasets mainly from (bio)medical imaging, where they exhibit excellent performance, and reliability, suggesting wide utility.
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| 4649. |
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| 4650. |
- Österman, Cecilia, 1971, et al.
(författare)
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Conceptual and Practical Strategy Work to Promote Ergonomics/Human Factors in Sweden
- 2019
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Ingår i: Advances in Intelligent Systems and Computing. - Cham : Springer International Publishing. - 2194-5365 .- 2194-5357. - 9783319960791 ; 821, s. 320-329
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Konferensbidrag (refereegranskat)abstract
- This paper describes the results of the conceptual and practical strategy work performed by the Swedish Ergonomics and Human Factors Society (EHSS) today. The rationale of EHSS is to strengthen the quality of ergonomics/human factors knowledge and practice in Sweden and form a multidisciplinary platform across disciplines and professions for collaboration and for knowledge sharing. EHSS gathers about 350 members, representing different occupations in industry, academia and the public sector. Together, EHSS members hold knowledge and experience in physical, cognitive and organizational ergonomics and its application in working life and society. The overall aim of this paper is to inspire related societies and stakeholders to initiate discussions about strategies and future projects that allow for collaboration and knowledge sharing. Proposedly follow the EHSS model where we have formed a multidisciplinary platform for collaboration across disciplines and professions. The activities initiated and supported by EHSS are one step towards broadening the knowledge and application of HFE in Sweden, and to comprise new arenas of specialization. By participating in the key areas in society such as teaching, standardization, product development and occupational safety and health, the work of EHSS is one piece of the puzzle to improve human activities in the future. The vision is that together, we can improve safety, efficiency and well-being for all.
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