Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
On active contour models and balloons
CVGIP: Image Understanding
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
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A novel region active contour model (ACM) for image segmentation is proposed in this paper. In order to perform an accurate segmentation of images with non-homogeneous intensity, the original region fitting energy in the general region-based ACMs is improved by an anisotropic region fitting energy to evolve the contour. Using the local image information described by the structure tensor, this new region fitting energy is defined in terms of two anisotropic fitting functions that approximate the image intensity along the principal directions of variation of the intensity. Therefore, the anisotropic fitting functions extract intensity information more precisely, which enable our model to cope with the boundaries with low-contrast and complicated structures. It is incorporated into a variational formula with a total variation (TV) regularization term with respect to level set function, from which the segmentation process is performed by minimizing this variational energy functional. Experiments on the vessel and brain magnetic resonance images demonstrate the advantages of the proposed method over Chan-Vese (CV) active contours and local binary active contours (LBF) in terms of both efficiency and accuracy.