Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3-step algorithm using region-based active contours for video objects detection
EURASIP Journal on Applied Signal Processing
| Hi-index | 0.00 |
Levelset methods were introduced in medical images segmentation by Malladi et al. in 1995. In this paper, we propose several improvements of the original method to speed up the algorithm convergence and to improve the quality of the segmentation in the case of cardiac gated SPECT images. We studied several evolution criterions, taking into account the dynamic property of heart image sequences. For each step of the segmentation algorithm, we have compared different solutions in order to both reduce time and improve quality. We have developed a modular segmentation tool with 3D+T visualization capabilities to experiment the proposed solutions and tune the algorithm parameters. We show segmentation results on both simulated and real SPECT images.