Image segmentation based on the hybrid bias field correction
From MaRDI portal
Publication:6105997
DOI10.1016/j.amc.2023.128050OpenAlexW4367323085MaRDI QIDQ6105997
Yue Li, Zhi-Feng Pang, Zhenyan Guan, Hong Ge, Ke Chen
Publication date: 27 June 2023
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2023.128050
image segmentationalternating direction methodintensity inhomogeneityadditive bias fieldmultiplicative bias field
Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Communication, information (94Axx) Computing methodologies and applications (68Uxx)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Image segmentation using Euler's elastica as the regularization
- Image segmentation with dynamic artifacts detection and bias correction
- Geodesic active contours
- A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity
- An efficient iterative thresholding method for image segmentation
- Global convergence of ADMM in nonconvex nonsmooth optimization
- A multiphase level set framework for image segmentation using the Mumford and Shah model
- A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation
- Effective two-stage image segmentation: a new non-Lipschitz decomposition approach with convergent algorithm
- A Retinex-based total variation approach for image segmentation and bias correction
- A retinex modulated piecewise constant variational model for image segmentation and bias correction
- Piecewise-smooth image segmentation models with \(L^1\) data-fidelity terms
- Generalized alternating direction method of multipliers: new theoretical insights and applications
- An efficient local Chan-Vese model for image segmentation
- Active contours without edges
- The $L_{0}$ Regularized Mumford–Shah Model for Bias Correction and Segmentation of Medical Images
- Image Segmentation for Intensity Inhomogeneity in Presence of High Noise
- Efficient and Convergent Preconditioned ADMM for the Potts Models
- Proximal ADMM for Euler's Elastica Based Image Decomposition Model
- A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI
- An Alternating Direction Algorithm for Total Variation Reconstruction of Distributed Parameters
- Variational Fuzzy Mumford–Shah Model for Image Segmentation
