Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic models for generic images
Quarterly of Applied Mathematics
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coupled Minimization Problem for Medical Image Segmentation with Priors
International Journal of Computer Vision
MAC: Magnetostatic Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Gait recognition via optimally interpolated deformable contours
Pattern Recognition Letters
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We propose in this paper a boundary finding scheme for biomedical imagery which integrates a region-based method and an edge-based technique. We show that more accurate and robust results may be obtained through seeking a joint solution to the traditional approach of curve evolution. The approach incorporates an energy model based on prior distribution and likelihood into the curve evolution of the geodesic active contour (GAC) method. During curve evolution, we use a decision function to adjust relevant parameters in the model automatically so that the curve can easily avoid 'clutter'. For termination of curve evolution, a stability index is proposed which examines curve evolution convergence to ensure that the curve arrives at the boundary robustly and accurately. The experimental results demonstrate that advantages can be achieved using our approach compared to several classical methods.