Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
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 t…
global-optimizationFast computation of mutual information in the frequency domain with applications to global multimodal image alignment
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 an…
GPUDisentangle-then-Align: Non-Iterative Hybrid Multimodal Image Registration via Cross-Scale Feature Disentanglement
Multimodal image registration is a fundamental task and a prerequisite for downstream cross-modal analysis. Despite recent progress in shared feature extraction and multi-scale architectures, two key limitations remain. …
Parameter PredictionImage RegistrationCoarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature…
CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-to-fine framework CORE for accurate nucle…
Image Registration