Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A variational level set approach to multiphase motion
Journal of Computational Physics
Level set methods: an overview and some recent results
Journal of Computational Physics
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Energy minimization based segmentation and denoising using a multilayer level set approach
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Segmentation of thin structures in volumetric medical images
IEEE Transactions on Image Processing
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Level set methods have been proven to be efficient tools for tracing interface problems. Recently, some variants of the Osher- Sethian level set methods, which are called the Piecewise Constant Level Set Methods (PCLSM), have been proposed for some interface problems. The methods need to minimize a smooth cost functional under some special constraints. In this paper a PCLSM for 3D image segmentation is tested. The algorithm uses the gradient descent method combined with a Quasi-Newton method to minimize an augmented Lagrangian functional. Experiments for medical image segmentation are shown on synthetic three dimensional MR brain images. The efficiency of the algorithm and the quality of the obtained images are demonstrated.