Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Gradient Vector Flow Fast Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Deformable Surfaces with Level Sets
IEEE Computer Graphics and Applications
A New Active Contour Method Based on Elastic Interaction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MAC: Magnetostatic Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contouring based on gradient vector interaction and constrained level set diffusion
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Geometrically Induced Force Interaction for Three-Dimensional Deformable Models
IEEE Transactions on Image Processing
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Segmentation in high dimensional space, e.g. 4D, often requires decomposition of the space and sequential data process, for instance space followed by time. In [1], the authors presented a deformable model that can be generalized into arbitrary dimensions. However, its direct implementation is computationally prohibitive. The more efficient method proposed by the same authors has significant overhead on computer memory, which is not desirable for high dimensional data processing. In this work, we propose a novel approach to formulate the computation to achieve memory efficiency, as well as improving computational efficiency. Numerical studies on synthetic data and preliminary results on real world data suggest that the proposed method has a great potential in biomedical applications where data is often inherently high dimensional.