International Journal of Computer Vision
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
Narrow band region-based active contours and surfaces for 2D and 3D segmentation
Computer Vision and Image Understanding
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
Quasi-automatic initialization for parametric active contours
Pattern Recognition Letters
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Contrast Constrained Local Binary Fitting for Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Snakes, shapes, and gradient vector flow
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
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution
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
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Active contour model (ACM) has been widely used in image segmentation, but the local region based ACM suffers from the sensitivity of the curve initialization, which means that the segmentation can be influenced by the initialized contour greatly. In this paper, we propose a novel local region based ACM. Firstly, we analyze the reason for the sensitivity of the curve initialization, i.e., the blind region and the false edges. Secondly, we propose a novel local region-based linear speed function, in which, the additive factor can solve the blind region and false edge problems, and the multiplicative factor can further improve the additive factor in solving false edge problem. Thirdly, we incorporate the proposed linear speed function into the local approximated signed distanced function based local segmentation framework. In the proposed method, we only need to select one point anywhere inside the object for initialization, which is very convenient for interactive segmentation. Experiments on synthetic and Magnetic Resonance (MR) brain images demonstrate the robustness of the initialization over the ACM driven by the classic local region-based intensity energy, ACM driven by local and global intensity energy and ACM driven by contrast constrained local intensity fitting energy.