scholarly journals Fractional Distance Regularized Level Set Evolution With Its Application to Image Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 84604-84617 ◽  
Author(s):  
Meng Li ◽  
Yi Zhan ◽  
Yongxin Ge
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Evolution ◽  
Fractional Distance

Adaptive Distance Preserving Level Set Evolution for Image Segmentation

2009 ◽  
Vol 19 (12) ◽  
pp. 3161-3169 ◽  
Author(s):  
Chuan-Jiang HE ◽  
Meng LI ◽  
Yi ZHAN
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Evolution

scholarly journals Image Segmentation using Level Set Evolution Driven by Complex Wavelets

2016 ◽  
Vol 9 (26) ◽  
Author(s):  
G. Raghotham Reddy ◽  
B. Narsimha ◽  
B. Rajender Naik ◽  
Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Complex Wavelets ◽  
Level Set Evolution

scholarly journals Shape Prior Embedded Level Set Model for Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Wansuo Liu ◽  
Dengwei Wang ◽  
Wenjun Shi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Geometric Transformation ◽  
Probability Weighting ◽  
Shape Prior ◽  
Factors Affecting ◽  
Sensitivity Problem ◽  
Level Set Evolution ◽  
Region Information ◽  
Level Set Model

This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.

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