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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Evaluation on the Compactness of Supervoxels
摘要： Supervoxels are perceptually meaningful atomic spatio-temporal regions in videos, which has great potential to reduce the computational complexity of downstream video applications. Many methods have been proposed for generating supervoxels. To effectively evaluate these methods, a novel supervoxel library and benchmark called LIBSVX with seven collected metrics was recently established. In this paper, we propose a new compactness metric which measures the shape regularity of supervoxels and is served as a necessary complement to the existing metrics. To demonstrate its necessity, we first explore the relations between the new metric and existing ones. Correlation analysis shows that the new metric has a weak correlation with (i.e., nearly independent of) existing metrics, and so reflects a new characteristic of supervoxel quality. Second, we investigate two real-world video applications. Experimental results show that the new metric can effectively predict some important application performance, while most existing metrics cannot do so.
关键词： metric evaluation,Supervoxel,video segmentation,compactness
更新于2025-09-19 17:15:36
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Accelerating multi-modal image registration using a supervoxel-based variational framework
摘要： For the successful completion of medical interventional procedures, several concepts, such as daily positioning compensation, dose accumulation or delineation propagation, rely on establishing a spatial coherence between planning images and images acquired at different time instants over the course of the therapy. To meet this need, image-based motion estimation and compensation relies on fast, automatic, accurate and precise registration algorithms. However, image registration quickly becomes a challenging and computationally intensive task, especially when multiple imaging modalities are involved. In the current study, a novel framework is introduced to reduce the computational overhead of variational registration methods. The proposed framework selects representative voxels of the registration process, based on a supervoxel algorithm. Costly calculations are hereby restrained to a subset of voxels, leading to a less expensive spatial regularized interpolation process. The novel framework is tested in conjunction with the recently proposed EVolution multi-modal registration method. This results in an algorithm requiring a low number of input parameters, is easily parallelizable and provides an elastic voxel-wise deformation with a subvoxel accuracy. The performance of the proposed accelerated registration method is evaluated on cross-contrast abdominal T1/T2 MR-scans undergoing a known deformation and annotated CT-images of the lung. We also analyze the ability of the method to capture slow physiological drifts during MR-guided high intensity focused ultrasound therapies and to perform multi-modal CT/MR registration in the abdomen. Results have shown that computation time can be reduced by 75% on the same hardware with no negative impact on the accuracy.
关键词： multi-modal registration,non-rigid registration,supervoxel,variational method
更新于2025-09-10 09:29:36