Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Processing Techniques for Tumor Detection
Image-Processing Techniques for Tumor Detection
Pattern Recognition and Image Preprocessing
Pattern Recognition and Image Preprocessing
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Medical Image Segmentation Based on the Bayesian Level Set Method
Medical Imaging and Informatics
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
IEEE Transactions on Information Technology in Biomedicine
Multidirectional and multiscale edge detection via M-band wavelet transform
IEEE Transactions on Image Processing
Area and length minimizing flows for shape segmentation
IEEE Transactions on Image Processing
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
Level set-based bimodal segmentation with stationary global minimum
IEEE Transactions on Image Processing
A Region Merging Prior for Variational Level Set Image Segmentation
IEEE Transactions on Image Processing
A novel method to look for the hysteresis thresholds for the Canny edge detector
Pattern Recognition
Unsupervised 2D gel electrophoresis image segmentation based on active contours
Pattern Recognition
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This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.