Split Bregman method for minimization of improved active contour model combining local and global information dynamically

DOI10.1016/j.jmaa.2011.11.073zbMath1232.68156OpenAlexW2095574171MaRDI QIDQ663708

Boying Wu, Yunyun Yang

Publication date: 27 February 2012

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmaa.2011.11.073

zbMATH Keywords

level set methodvariational methodimage segmentationactive contour modelsplit Bregman methodintensity inhomogeneity

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Machine vision and scene understanding (68T45)

Related Items (8)

A method for lung boundary correction using split Bregman method and geometric active contour model ⋮ Parallelizable and robust image segmentation model based on the shape prior information ⋮ Accurate and efficient image segmentation and bias correction model based on entropy function and level sets ⋮ Brain MR image segmentation based on an adaptive combination of global and local fuzzy energy ⋮ Median filter based compressed sensing model with application to MR image reconstruction ⋮ Split Bregman method for minimization of fast multiphase image segmentation model for inhomogeneous images ⋮ Active contour model driven by global and local intensity information for ultrasound image segmentation ⋮ Level set formulation based on edge and region information with application to accurate lesion segmentation of brain magnetic resonance images

Uses Software

• RecPF

Cites Work

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• A modified Chan-Vese model and its theoretical proof
• Optimal approximations by piecewise smooth functions and associated variational problems
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
• The Split Bregman Method for L1-Regularized Problems
• Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
• Active contours without edges
• Minimization of Region-Scalable Fitting Energy for Image Segmentation
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• An Iterative Regularization Method for Total Variation-Based Image Restoration

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