自适应熵疫苗算子的免疫图像分割方法

Immune image segmentation algorithm based on adaptive entropy vaccine operator

摘要:

本文针对二维熵图像分割算法存在计算量巨大的问题,基于免疫调节机制,设计了一种新的图像分割方法．该方法利用免疫疫苗理论,并与熵理论相结合,提出了熵疫苗算子．熵疫苗作为抗体疫苗进行选择,通过熵运算,提取更新疫苗库,从而使得最优分割阈值的形成及疫苗库的更新具有自适应性,疫苗的选择更加准确有效．仿真实验表明熵疫苗算子加快了算法的搜索速度、提高了算法的适应性．该算法对复杂图像具有良好分割效果和较强的实时处理能力．

Abstract:

The two-dimensional entropy image segmentation algorithm has a huge amount of computation problems. Based on immune adjustment mechanism, a new image segmentation algorithm is designed to solve the problem in this paper. In this method, vaccine theory is combined with entropy theory to propose the entropy vaccine operator. Entropy vaccine operator is selected as an antibody vaccine. Extract updated vaccine bank by using entropy calculation. In this way can make the formation of optimal segmentation threshold and the update of vaccine library with adaptive, and the select of vaccine more accuracy and more effective. The simulation results indicate that the searching speed of algorithm is accelerated and the adaptability is increased due to entropy genetic operator. The algorithm is approved to have good segmentation effect and strong real-time processing capability.