An Improved Adaptive Level Set Method for Image Segmentation

Author(s):  
Li Zhang ◽  
Kai-Teng Wu ◽  
Ping Li

In order to improve the accuracy of image segmentation, an improved adaptive level set method is proposed based on level set evolution without re-initialization method and adaptive distance preserving level set evolution method. A new definition of weight coefficient in evolution equations is the main innovation of this paper. The improved method can detect certain object boundaries, interior and exterior contours of an object, edges of multi-objects and weak boundaries of an object by synthetic and real images numerical experiments. Numerical results show that the improved adaptive level set method has faster segmentation speed and higher segmentation accuracy compared with the previous two methods, especially in weak boundaries and edges of multi-objects segmentation problems.

2014 ◽  
Vol 981 ◽  
pp. 368-371
Author(s):  
Guang Yu Liu ◽  
Yong Jie Pang ◽  
Hong Yu Bian ◽  
En Ming Zhao

To solve the problem that many image segmentation methods cannot be applied to forward looking sonar image accurately, an improved level set segmentation method was proposed in this paper. Firstly, the level set evolution without re-initialization was introduced. Secondly the different characteristics of forward looking sonar image from the optical image were analyzed, and we got the factors affecting segmentation. Then, to overcome these negative effects, this paper did preprocessing by morphological top-hat and bottom-hat transformation, and carried on level set method without re-initialization to construct an improved level set sonar image segmentation system. Finally, our method was compared with the traditional level set method in computer experiments. Simulation results show that it is more adapted to forward looking sonar image segmentation.


2009 ◽  
Vol 19 (12) ◽  
pp. 3161-3169 ◽  
Author(s):  
Chuan-Jiang HE ◽  
Meng LI ◽  
Yi ZHAN

2017 ◽  
Vol 32 (4) ◽  
pp. 407-421
Author(s):  
Qiong Lou ◽  
Jia-lin Peng ◽  
De-xing Kong ◽  
Chun-lin Wang

2016 ◽  
Vol 9 (26) ◽  
Author(s):  
G. Raghotham Reddy ◽  
B. Narsimha ◽  
B. Rajender Naik ◽  
Rameshwar Rao

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