A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts

This paper presents a novel Markov random field (MRF) and adaptive regularization embedded level set model for robust image segmentation and uses graph cuts optimization to numerically solve it. Firstly, a special MRF-based energy term in the form of level set formulation is constructed for strong l...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2019-01, Vol.2019 (2019), p.1-13
Main Author: Wang, Dengwei
Format: Article
Language:English
Subjects:
Algorithms
Constraint modelling
Convergence
Energy
Fields (mathematics)
Image segmentation
International conferences
Markov chains
Medical research
Methods
Noise
Noise reduction
Optimization
Regularization
Robustness (mathematics)
Citations: Items that this one cites
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Description
Summary:This paper presents a novel Markov random field (MRF) and adaptive regularization embedded level set model for robust image segmentation and uses graph cuts optimization to numerically solve it. Firstly, a special MRF-based energy term in the form of level set formulation is constructed for strong local neighborhood modeling. Secondly, a regularization constraint with adaptive properties is imposed onto the proposed model with the following purposes: reduce the influence of noise, force the power exponent of the regularization process to change adaptively with image coordinates, and ensure the active contour does not pass through the weak object boundaries. Thirdly, graph cuts optimization is used to implement the numerical solution of the proposed model to obtain extremely fast convergence performance. The extensive and promising experimental results on wide variety of images demonstrate the excellent performance of the proposed method in both segmentation accuracy and convergence rate.
ISSN:2090-0147
2090-0155
DOI:10.1155/2019/8747385