Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Statistical shape analysis of anatomical structures
Statistical shape analysis of anatomical structures
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
A generalized level set formulation of the mumford-shah functional for brain MR image segmentation
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
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Image segmentation is an important research topic in medical image analysis area. In this paper, we firstly propose a generalized level set formulation of the Mumford-Shah functional by a sound mathematical definition of line integral. The variational flow is implemented in level set framework and thus implicit and intrinsic. By embedding a weighted length term to the original Mumford-Shah functional, the paper presents a generic framework that integrates region, gradient and shape information of an image into the segmentation process naturally. The region force provides a global criterion and increases the speed of convergence, the gradient information allows for a better spatial localization while the shape prior makes the model especially useful to recover objects of interest whose shape can be learned through statistical analysis. The shape prior is represented by the zero-level set of signed distance maps of images and is well consistent with level set based variational framework. Experiments on 2-D synthetic and real images validate this novel method.