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
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Shortest Paths on Surfaces Using Level Sets Propagation
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
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
Optical aerial image partitioning using level sets based on modified Chan-Vese model
Pattern Recognition Letters
Texture-based parametric active contour for target detection and tracking
International Journal of Imaging Systems and Technology
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Unsupervised hierarchical image segmentation with level set and additive operator splitting
Pattern Recognition Letters
A topology preserving level set method for geometric deformable models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
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In this article, we propose a novel model to overcome the drawbacks of the modified Chan–Vese (MCV) model. Our model is devoted to find an optimal partition of inhomogeneous regions accurately and computationally efficient. MCV model was proposed on the concept of using one level-set function for one region. It needs fewer numbers of iterations and improves the efficiency of image segmentation in contrast to the multiphase Chan–Vese model. The MCV model, however, is highly dependent on initial curves placement and often leads to erroneous segmentations on images with intensity inhomogeneity. In our model, to eliminate the affection of background information on the curve evolution and speed up the curve evolution, we first use the k-means algorithm to presegment the image to get the initial curves and then add the local image information to the total energy functional of MCV model to deal with the intensity inhomogeneity. Finally, extensive experiments are done and the segmentation results on homogeneous multiphase images verify that the proposed method has the better accuracy and efficiency comparing to MCV model. Moreover, we show results on challenging multiphase inhomogeneous image to illustrate the robust and accurate segmentation that are possible with this novel model. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 103–113, 2012 © 2012 Wiley Periodicals, Inc.