The Optimized Level Set Image Segmentation Based on Saliency Maps
Wan-ru Li, Feng Ye, Jiazhen Chen, Zihua Zheng · 2018
In order to improve the practicability of the level set method and reduce the computational cost, a optimized level set active contour model that embeds the image local information is proposed in this paper. Firstly, the optimal saliency method of the image is selected by comparing three saliency methods, which is helped to generate the initial contour of the image. Secondly, a new variational level set model integrating edge information and regional local information is presented, and a local energy term is added to the energy function. Finally, image segmentation is implemented by new level set methods based on optimal saliency method. Experiments demonstrate the results of the new level set method based on optimal saliency method are higher than the Distance Regularized Level Set Evolution (DRLSE) model in terms of both efficiency and accuracy. Moreover, the segmentation time of the optimization algorithm only needs 1.94% of the former, and it has high segmentation accuracy.