International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Segmentation of interest region in medical volume images using geometric deformable model
Computers in Biology and Medicine
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
Elastic image registration using hierarchical spatially based mean shift
Computers in Biology and Medicine
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Appropriate initialization and stable evolution are desirable criteria to satisfy in level set methods. In this study, a novel region-based level set method utilizing both global and local image information complementarily is proposed. The global image information is extracted from mean shift clustering without any prior knowledge. Appropriate initial contours are obtained by regulating the clustering results. The local image information, as extracted by a data fitting energy, is employed to maintain a stable evolution of the zero level set curves. The advantages of the proposed method are as follows. First, the controlling parameters of the evolution can be easily estimated by the clustering results. Second, the automaticity of the model increases because of a reduction in computational cost and manual intervention. Experimental results confirm the efficiency and accuracy of the proposed method for medical image segmentation.