Search Results for author: Johan Öfverstedt

Found 8 papers, 7 papers with code

A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms

no code implementations13 May 2024 Johan Öfverstedt, Elin Lundström, Göran Bergström, Joel Kullberg, Håkan Ahlström

The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging.

Image Registration Image Segmentation +1

Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields

1 code implementation19 Oct 2021 Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

The method is fast; a 3. 4Mvoxel global rigid alignment 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.

Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment

1 code implementation28 Jun 2021 Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

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.

Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study

2 code implementations30 Mar 2021 Jiahao Lu, Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.

Generative Adversarial Network Image Registration +3

INSPIRE: Intensity and spatial information-based deformable image registration

1 code implementation14 Dec 2020 Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods.}

Computational Efficiency Image Registration

CoMIR: Contrastive Multimodal Image Representation for Registration

1 code implementation NeurIPS 2020 Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje

We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).

Image-to-Image Translation

Stochastic Distance Transform

2 code implementations18 Oct 2018 Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise.

Template Matching

Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

2 code implementations30 Jul 2018 Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

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).

Image Registration Medical Image Registration

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