A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Region-based strategies for active contour models
International Journal of Computer Vision
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Digital Image Processing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Local Statistic Based Region Segmentation with Automatic Scale Selection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Lennard-Jones force field for geometric active contour
Signal Processing
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
A Relay Level Set Method for Automatic Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Area and length minimizing flows for shape segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
| Hi-index | 0.08 |
Due to the material property and imperfections of imaging devices, noise often exists in real-world images. This paper presents an improved region-based active contour model-Robust Chan-Vese (RCV) model for noisy image segmentation. First, for each point in a region, a local energy is defined according to the difference between the intensities of all points within its neighborhood and the intensity average of the region. Then, for the whole image domain, a global energy is defined as a data term to integrate the local energy with respect to the neighborhood center. Finally, the overall energy is represented by a level set formulation, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data term, intensity information in local region is taken into account to guide the motion of contour, which enables RCV model to cope with noise. The improved method has been evaluated on synthetic image and industrial CT images. Compared with several popular level set methods, the experimental results show that RCV model is not only less sensitive to contour initialization, but also more robust to image noise while preserving the segmentation efficacy.