ATP-MLSM: angular texture pattern-multi-level set model-based retinal image segmentation approach

2016 ◽  
Vol 22 (4) ◽  
pp. 285
Author(s):  
Arti Taneja ◽  
Priya Ranjan ◽  
Amit Ujlayan
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Retinal Image ◽  
Texture Pattern ◽  
Model Based ◽  
Segmentation Approach ◽  
Multi Level ◽  
Retinal Image Segmentation ◽  
Level Set Model

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.

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.

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