Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level set methods for curvature flow, image enhancement, and shape recovery in medical images
Visualization and mathematics
A Memory and Computation Efficient Sparse Level-Set Method
Journal of Scientific Computing
A 2D moving grid geometric deformable model
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Point-Based geometric deformable models for medical image segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Segmentation of interest region in medical volume images using geometric deformable model
Computers in Biology and Medicine
ImaGiNe Seldinger: First simulator for Seldinger technique and angiography training
Computer Methods and Programs in Biomedicine
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An efficient adaptive multigrid level set method for front propagation purposes in three dimensional medical image segmentation is presented. It is able to deal with non sharp segment boundaries. A flexible, interactive modulation of the front speed depending on various boundary and regularization criteria ensure this goal. Efficiency is due to a graded underlying mesh implicitly defined via error or feature indicators. A suitable saturation condition ensures an important regularity condition on the resulting adaptive grid. As a casy study the segmentation of glioma is considered. The clinician interactively selects a few parameters describing the speed function and a few seed points. The automatic process of front propagation then generates a family of segments corresponding to the evolution of the front in time, from which the clinician finally selects an appropriate segment covered by the gliom. Thus, the overall glioma segmentation turns into an efficient, nearly real time process with intuitive and usefully restricted user interaction.