Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A variational level set approach to multiphase motion
Journal of Computational Physics
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Medical Image Segmentation Using New Hybrid Level-Set Method
MEDIVIS '08 Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
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
Efficient segmentation of piecewise smooth images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A level set based segmentation method for images with intensity inhomogeneity
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Active contour method combining local fitting energy and global fitting energy dynamically
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Unsupervised texture segmentation with nonparametric neighborhood statistics
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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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Intensity inhomogeneity often appears in medical images, such as X-ray tomography and magnetic resonance (MR) images, due to technical limitations or artifacts introduced by the object being imaged. It is difficult to segment such images by traditional level set based segmentation models. In this paper, we propose a new level set method integrating local and global intensity information adaptively to segment inhomogeneous images. The local image information is associated with the intensity difference between the average of local intensity distribution and the original image, which can significantly increase the contrast between foreground and background. Thus, the images with intensity inhomogeneity can be efficiently segmented. What is more, to avoid the re-initialization of the level set function and shorten the computational time, a simple and fast level set evolution formulation is used in the numerical implementation. Experimental results on synthetic images as well as real medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method.