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
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Efficient segmentation of piecewise smooth images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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The segmentation of brain magnetic resonance (MR) images is of great significance in research and clinical applications, including diagnosis of pathology, presurgical planning and computer integrated surgery. To cope with the inevitable intensity inhomogeneity problem of MR images, based on the techniques of curve evolution and level sets, a novel curve evaluation method employing neighboring information was proposed in this paper. An energy function was defined with a local intensity fitting term, which adopted the local information in the neighborhood of the interested pixels, and greatly improved the boundary recognition accuracy for low contrast areas. A numerical algorithm using finite difference is also presented. Finally, the method was tested by several experiments on MR images. Experimental results indicate the proposed algorithm is effective for segmenting MR images corrupted by intensity inhomogeneity, and usually outperforms the corresponding conventional methods. The proposed model could also be applied in segmentation of ordinary inhomogeneous images which is very common in reality.