TITLE:
An Improved Active Contour Model Based on Local Information
AUTHORS:
Weiqin Chen, Changjiang Liu, Bin Pan
KEYWORDS:
Image Segmentation, Active Contour, Level Set, Local Fitting, Optimize Log
JOURNAL NAME:
Open Access Library Journal,
Vol.8 No.2,
February
25,
2021
ABSTRACT: In view of the problem that the local active contour model is difficult to achieve image segmentation accurately and quickly, an improved image segmentation method based on Local Image Fitting (LIF) is proposed. Firstly, the local median is used as the fitting center of the curve to enhance the robustness of the model to noise. Secondly, a minimized Laplacian of gaussian energy (Log) term is introduced, and the Log operator is used to smooth the image and enhance the edges of the image. Finally, the minimized Log energy term is combined with the LIF, which together drives the curve to the boundary. Experimental results show that the Precision rate, Recall rate and Dice Similarity Coefficient of this model are closest to 1. Compared with other main region-based models, the image segmentation accuracy of this method is significantly higher than that of other algorithms, which improves the anti-noise performance and image segmentation speed.