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
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geometrical image segmentation by the Allen-Cahn equation
Applied Numerical Mathematics
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
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Initialization Techniques for Segmentation with the Chan-Vese Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
On the statistical interpretation of the piecewise smooth Mumford-Shah functional
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Multiphase image segmentation using a phase-field model
Computers & Mathematics with Applications
Energy minimization based segmentation and denoising using a multilayer level set approach
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Snakes, shapes, and gradient vector flow
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
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
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Angiograms have been extensively used by neurosurgeons for vascular and non-vascular pathology. Indeed, examining the cerebral vessel network is helpful in revealing arteriosclerosis, diabetes, hypertension, cerebrovascular diseases and strokes. Thus, accurate segmentation of blood vessels in the brain is of major importance to radiologists. Many algorithms have been proposed for blood vessel segmentation. Although they work well for segmenting major parts of vessels, these techniques cannot handle challenging problems including (a) segmentation of thinner blood vessels due to low contrast around thin blood vessels; (b) inhomogeneous intensities, which lead to inaccurate segmentation. In order to tackle these challenges, we developed a new Allen Cahn (AC) equation and likelihood model to segment blood vessels in angiograms. Its level set formulation combines length, region-based and regularization terms. The length term is represented by the AC equation with a double well potential. The region-based term combines both local and global statistical information, where the local part deals with the intensity inhomogeneity, and the global part solves the low contrast problem. Finally, the regularization term ensures the stability of contour evolution. Experimental results show that the proposed method is both efficient and robust, and is able to segment inhomogeneous images with an arbitrary initial contour. It outperforms other methods in detecting finer detail.