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Active contour model with improved second-order differential driven term
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AI
DOI：10.1016/j.sigpro.2024.109667.png)
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Abstract
En 中文
Driven terms in active contour models (ACMs) play a significant role in edge identification and image segmentation. However, in many existing ACMs, the driven terms are iteratively updated, resulting in slower segmentation speed because the image segmentation time increases with the iteration number. To address this problem, an ACM based on an improved second-order differential driven term (ISDDT) is presented, which can extract the edge information of images. The improved second-order differential driven term is computed only once before the iterations. Therefore, the computational complexity of our presented ACM is reduced, leading to a faster image segmentation speed. In addition, an improved regularization method with mean filtering is presented to improve the robustness of our ISDDT model. As an application, a target contour tracking method is developed based on our ISDDT model. Experimental results show that our ISDDT model segments images with inhomogeneous intensities well. The image segmentation speed of our proposed model has obvious advantages.
Keywords:
Active contour model
Intensity inhomogeneity
Second-order differential driven term
Laplacian of Gaussian filtering