An Innovative Variational Level Set Model for Multiphase Image Segmentation

2009 ◽  
Author(s):  
Jie Shi ◽  
Zhenkuan Pan ◽  
Weibo Wei
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Variational Level Set ◽  
Level Set 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 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.

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