scholarly journals Fast Mumford-Shah Two-Phase Image Segmentation Using Proximal Splitting Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Nilima Shah ◽  
Dhanesh Patel ◽  
Pasi Fränti
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
Phase Image ◽  
Splitting Scheme ◽  
Two Phase ◽  
Crucial Step ◽  
Error Metrics ◽  
Initial Segmentation

The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metrics shows that 70 percent of the segmentations keep the error values below 50%. Compared to the level set method to solve the Chan-Vese model, our algorithm is significantly faster. At the same time, it gives almost the same or better segmentation results. When compared to the recent k-means variant, it also gives much better segmentation with convex boundaries. The proposed algorithm balances well between time and quality of the segmentation. A crucial step in the design of machine vision systems is the extraction of discriminant features from the images, which is based on low-level segmentation which can be obtained by our approach.

Two-Phase Image Segmentation with Nonlocal Mean Filter and Kernel Evolutionary Clustering in Local Learning

2014 ◽  
Vol 536-537 ◽  
pp. 172-175
Author(s):  
Bing Chen ◽  
Dong Dong Yang ◽  
Gang Lu ◽  
Hong Xiao Feng
Keyword(s):  
Image Segmentation ◽  
Learning Strategy ◽  
Phase Image ◽  
Local Learning ◽  
Two Phase ◽  
Evolutionary Clustering ◽  
Kernel Clustering ◽  
Mean Filter ◽  
Image Segmentation Algorithm

In this study, a novel two-phase image segmentation algorithm (TPIS) by using nonlocal mean filter and kernel evolutionary clustering in local learning is proposed. Currently, the difficulties for image segmentation lie in its vast pixels with overlapping characteristic and the noise in the different process of imaging. Here, we want to use nonlocal mean filter to remove different types of noise in the image, and then, two kernel clustering indices are designed in evolutionary optimization. Besides, the local learning strategy is designed using local coefficient of variation of each local pixels or image patch is employed to update the quality of the local segments. The new algorithm is used to solve different image segmentation tasks. The experimental results show that TPIS is competent for segmenting majority of the test images with high quality.

VARIATIONAL TWO-PHASE IMAGE SEGMENTATION BASED ON REGION COMPETITION BY LEVEL SET AND FUZZY APPROACHES

2011 ◽  
Author(s):  
Marcus Aurelio Vinicus R. Pereira Borges ◽  
Marcus Aurelio Batista ◽  
Celia A Zorzo Barcelos
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Phase Image ◽  
Two Phase

scholarly journals An Image Segmentation Method Based on Improved Regularized Level Set Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2393 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
External Energy ◽  
Regularization Term ◽  
Level Set Function ◽  
New Energy ◽  
Segmentation Approach ◽  
Set Function ◽  
Distance Regularization

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

Weighted variational two-phase image segmentation based on Fuzzy Region Competition

2011 ◽  
Author(s):  
Vinicius R. P. Borges ◽  
Celia A. Zorzo Barcelos ◽  
Denise Guliato ◽  
Marcos Aurelio Batista
Keyword(s):  
Image Segmentation ◽  
Phase Image ◽  
Two Phase ◽  
Fuzzy Region

Binary Level Set Methods for Dynamic Reservoir Characterization by Operator Splitting Scheme

2012 ◽  
Vol 4 (06) ◽  
pp. 780-798
Author(s):  
Changhui Yao
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Reservoir Characterization ◽  
Operator Splitting ◽  
Saddle Point Problem ◽  
Absolute Permeability ◽  
Point Problem ◽  
Splitting Scheme ◽  
Two Phase ◽  
The Absolute

AbstractIn this paper, operator splitting scheme for dynamic reservoir characterization by binary level set method is employed. For this problem, the absolute permeability of the two-phase porous medium flow can be simulated by the constrained augmented Lagrangian optimization method with well data and seismic time-lapse data. By transforming the constrained optimization problem in an unconstrained one, the saddle point problem can be solved by Uzawas algorithms with operator splitting scheme, which is based on the essence of binary level set method. Both the simple and complicated numerical examples demonstrate that the given algorithms are stable and efficient and the absolute permeability can be satisfactorily recovered.

Anisotropic Diffusion Combined with Nonconvex Functional for Noise Image Segmentation

2020 ◽  
pp. 2154009
Author(s):  
Ming Han ◽  
Jing-Qin Wang ◽  
Qian Dong ◽  
Jing-Tao Wang ◽  
Jun-Ying Meng
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Anisotropic Diffusion ◽  
Functional Model ◽  
Solution Space ◽  
Energy Functional ◽  
Noise Image ◽  
Nonconvex Energy ◽  
Nonconvex Functional ◽  
Level Set Model

Aiming at the problems of low segmentation accuracy of noise image, poor noise immunity of the existing models and poor adaptability to complex noise environment, a noise image segmentation algorithm using anisotropic diffusion and nonconvex functional was proposed. First, focusing on the “staircase effect”, a nonconvex functional was introduced into the energy functional model for smooth denoising. Second, the validity and accuracy of the model were established by proving that there was no global minimum in the solution space of the nonconvex energy functional model; the improved model was then used to obtain a smooth and clear image edge while maintaining the edge integrity. Third, the smooth image obtained from the nonconvex energy functional model was combined with the level set model to obtain the anisotropic diffusion gray level set model. The optimal outline of the target was obtained by calculating the minimum value of the energy functional. Finally, an anisotropic diffusion equation with nonconvex energy functional model was built in this algorithm to segment noise image accurately and quickly. A series of comparative experiments on the proposed algorithm and similar algorithms were conducted. The results showed that the proposed algorithm had strong noise resistance and provided precise segmentation for noise image.

scholarly journals A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Liming Tang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Energy Functional ◽  
Image Clustering ◽  
Clustering Model ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Variational Level Set ◽  
Level Set Model

The fuzzy C means clustering algorithm with spatial constraint (FCMS) is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

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