Micrographic Image Segmentation Using Level Set Model based on Possibilistic C-Means Clustering

Author(s):  
Nabil Chetih ◽  
Naim Ramou ◽  
Zoubeida Messali ◽  
Amina Serir ◽  
Yamina Boutiche
2014 ◽  
Vol 556-562 ◽  
pp. 4797-4801
Author(s):  
Yu Zhou ◽  
Wei Guo Zhang ◽  
Li Feng Li

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.


2010 ◽  
Vol 22 (1) ◽  
pp. 194-198
Author(s):  
景越峰 Jing Yuefeng ◽  
张小琳 Zhang Xiaolin ◽  
管永红 Guan Yonghong

Author(s):  
Sourour Gargouri ◽  
Aymen Mouelhi ◽  
Mounir Sayadi ◽  
Salam Labidi ◽  
Leila Ben Farhat ◽  
...  

2020 ◽  
Vol 105 ◽  
pp. 103174
Author(s):  
Asma Shamsi Koshki ◽  
Maryam Zekri ◽  
Mohammad Reza Ahmadzadeh ◽  
Saeed Sadri ◽  
Elham Mahmoudzadeh

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