一种基于Levy-人工蜂群的三维Otsu阈值分割算法

针对图像分割过程中三维Otsu算法运算时间长、计算量大的问题,提出了一种基于Levy-人工蜂群算法的三维Otsu阈值分割算法。首先,以像素灰度值-邻域均值-邻域中值的三维类间方差作为人工蜂群算法的适应度函数;其次,采用Levy飞行模式评价像素的适应度,对其种群更新及邻域搜索过程进行优化,以增强其全局搜索能力;最后,利用改进后的算法得到的分割阈值对图像进行分割。仿真实验结果表明,与传统三维Otsu阈值分割算法相比,所提算法能够有效降低图像存储空间,处理时间降低了30.8%,具备更好的抗噪性能,分割效果也更为理想。

A Levy-artificial bee colony based 3D Otsu threshold segmentation algorithm

In view of the problem of long operation time and large amount of calculation of the three-dimensional(3D) Otsu algorithm in the image segmentation process,a 3D Otsu threshold segmentation algorithm based on Levy-artificial bee colony algorithm is proposed.First,the 3D inter-class variance of pixel gray value-neighborhood mean-neighborhood median is used as the fitness function of the artificial bee colony algorithm.Second,the Levy flight model is used to evaluate the fitness of the pixel,and its population and neighborhood are updated.The search process is optimized to enhance its global search capabilities.Finally,the image is segmented using the segmentation threshold obtained by the improved algorithm.The simulation experiment results show that compared with the traditional 3D Otsu threshold segmentation algorithm,the proposed algorithm can effectively reduce the image storage space,the processing time is reduced by 30.8%,with better anti-noise performance and more ideal segmentation effect.