Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, Jianzhong Cao’s research team propose an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.

（Original research article "Journal of Electrical Engineering & Technology Vol. 14, Issue 1, pp. 445-453 (2019) https://link.springer.com/article/10.1007%2Fs42835-018-00030-8"）