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.

A Variational Level Set Model Based on Local Clustering for Image Segmentation

2014 ◽  
Vol 556-562 ◽  
pp. 4797-4801
Author(s):  
Yu Zhou ◽  
Wei Guo Zhang ◽  
Li Feng Li
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Criterion Function ◽  
Model Based ◽  
Local Clustering ◽  
Variational Level Set ◽  
Local Intensity ◽  
Clustering Criterion ◽  
Level Set Model

For images with intensity inhomogeneities that can’t get accurate segmentation results, this paper proposes a variational level set model based on local clustering. First,based on the model of images with intensity inhomogeneities, we use the K-mean clustering algorithm for intensity clustering in a neighborhood of each point of images with intensity inhomogeneities, and define a local clustering criterion function for the image intensities in the neighborhood. Then this local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. This criterion defines an energy function as a local intensity fitting term in the level set model. By minimizing this energy, our method is able to get the accurate image segmentation. The image segmentation results prove that our model in the aspect of segmenting images with intensity inhomogeneity is better than piecewise constant (PC) models, and the segmentation efficiency is higher than region-scalable fitting (RSF) model.

A variational level set model for multiscale image segmentation

2019 ◽  
Vol 493 ◽  
pp. 152-175 ◽  
Author(s):  
Honglu Zhang ◽  
Liming Tang ◽  
Chuanjiang He
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Variational Level Set ◽  
Multiscale Image Segmentation ◽  
Level Set Model

scholarly journals Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Energy Functional ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Proposed Model ◽  
Variational Level Set ◽  
Convex Image Segmentation ◽  
Level Set Formulation ◽  
Global Fitting

We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with other models also demonstrates the accuracy and superiority of the proposed model.

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 Combined Fuzzy Clustering and Chan_Vese Model for Multicolor Gradual Ochotona curzoniae Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haiyan Chen ◽  
Huaqing Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Heaviside Function ◽  
Dice Similarity Coefficient ◽  
Fuzzy C Means ◽  
Ochotona Curzoniae ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Fuzzy C Means Clustering ◽  
And Behavior

Precise segmentation of Ochotona curzoniae images collected in a nature scene is the basis of Ochotona curzoniae recognition and behavior analysis. Ochotona curzoniae images have the characteristics of diversity and graduality of target colors and complex background. The method of combined Chan_Vese model and k-means clustering algorithm is used to segment the multicolor images, but when k-means clustering algorithm is used to cluster the color of multicolor images, the manner of hard classification is adopted, without considering the color-gradient feature. As a resolution to this problem, a new approach of the Chan_Vese model in combination with fuzzy C-means clustering is proposed in the present paper. The proposed model utilises fuzzy C-means clustering to cluster the pixels inside the evolution curve of the Chan_Vese model, classifying the pixels into a certain color cluster with a certain probability to describe the image color gradual characteristics. By fuzzy C-means clustering, several cluster centers can be obtained, and the values of cluster centers can be used to replace internal fitting values of the Chan_Vese model. In this way, the problem that the Chan_Vese model cannot segment images with intensity inhomogeneity is overcome. Furthermore, the global Heaviside function is replaced by the local Heaviside function to suppress the influence of the background on image segmentation. The experimental results of Ochotona curzoniae images segmentation demonstrate that the proposed model can more accurately locate the target contour and has a higher Dice similarity coefficient, Jaccard Similarity, and segmentation accuracy.

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