Optical aerial image partitioning using level sets based on modified Chan-Vese model
Pattern Recognition Letters
Image Segmentation and Selective Smoothing Based on Variational Framework
Journal of Signal Processing Systems
A narrow band graph partitioning method for skin lesion segmentation
Pattern Recognition
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Multiphase Segmentation Based on Implicit Active Shape Models
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Text line segmentation in handwritten documents using Mumford-Shah model
Pattern Recognition
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
A local probabilistic prior-based active contour model for brain MR image segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A level set based segmentation method for images with intensity inhomogeneity
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Unsupervised multiphase segmentation: a phase balancing model
IEEE Transactions on Image Processing
Unsupervised multiphase segmentation: a phase balancing model
IEEE Transactions on Image Processing
A modified support vector machine and its application to image segmentation
Image and Vision Computing
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Segmentation of ultrasonic images of the carotid
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Images boundary extraction based on curve evolution and ant colony algorithm
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels
Pattern Recognition
Image segmentation using level set and local linear approximations
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Statistical Density Estimation Using Threshold Dynamics for Geometric Motion
Journal of Scientific Computing
Unsupervised edge detection and noise detection from a single image
Pattern Recognition
A novel multi-scale local region model for segmenting image with intensity inhomogeneity
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
A nonlinear level set model for image deblurring and denoising
The Visual Computer: International Journal of Computer Graphics
Hi-index | 0.01 |
Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results.