Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

Abstract

To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation.