Application of Otsu multi-threshold segmentation based on artificial bee colony and wolf pack algorithm optimization in optical images

Medical image segmentation is the process of accurately separating the patient area from the background area in medical images. High quality medical image segmentation helps to diagnose and treat patients more accurately. Otsu threshold segmentation algorithm has been used in optical image segmentation and continuously improved, but it has a high time complexity, and with the increase of threshold numbers, the time complexity shows an exponential growth trend, which has not been well solved. In response to this issue, this article combines artificial bee colonies with wolf pack algorithms for Otsu threshold segmentation. The directional wolf pack algorithm (WPA) is used to find the optimal threshold set in the solution space, while the artificial bee colony algorithm is used to pass on the excellent genes of wolf detection to improve convergence speed and maintain population diversity, further improving the segmentation efficiency and accuracy of optical images. The experiment verified that the fusion of artificial bee colony and wolf pack algorithm was applied to Otsu multi threshold segmentation, and the segmentation speed and accuracy were effectively improved.