Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Deformable B-solids and implicit snakes for 3D localization and tracking of SPAMM MRI data
Computer Vision and Image Understanding
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Outlier rejection in high-dimensional deformable models
Image and Vision Computing
LV Motion and Strain Computation from tMRI Based on Meshless Deformable Models
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
LV surface reconstruction from sparse TMRI using Laplacian surface deformation and optimization
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Information Theory
A 3D Laplacian-driven parametric deformable model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Deformable models have been widely used with success in medical image analysis. They combine bottom-up information derived from image appearance cues, with top-down shape-based constraints within a physics-based formulation. However, in many real world problems the observations extracted from the image data often contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue, we introduce a new family of deformable models that are inspired from compressed sensing, a technique for efficiently reconstructing a signal based on its sparseness in some domain. In this problem, we employ sparsity to represent the outliers or gross errors, and combine it seamlessly with deformable models. The proposed new formulation is applied to the analysis of cardiac motion, using tagged magnetic resonance imaging (tMRI), where the automated tagging line tracking results are very noisy due to the poor image quality. Our new deformable models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.