基于ABC-PCNN模型的图像分割

Image segmentation based on ABC-PCNN model

为使标准脉冲耦合神经网络(Pulse coupled neural network,PCNN)模型在图像分割中能够自适应地调整模型参数与全局阈值,提高分割效果,该文提出一种基于人工蜂群(Artificial bee colony,ABC)算法改进的自适应PCNN模型,即人工蜂群算法-脉冲耦合神经网络(ABCPCNN)模型;提出了改进后的乘积型交叉熵函数,并利用ABC算法将此函数作为其适应度函数优化输出其连接系数和阈值。采用Lena图像和血细胞图像评估PCNN模型和ABC-PCNN模型的性能。实验结果表明:ABC-PCNN模型对图像的自适应分割效果优于PCNN模型。针对血细胞分割图像中存在的重叠区域,该文结合角点和质点坐标定位重叠区域的二次分割线得到最终分割图像,所提算法高效且能得到较好的分割结果。

In order to adjust the model parameters and the global threshold for image segmentation, an improved pulse coupled neural network （ PCNN ） model based on artificial bee colony （ ABC ） algorithm,namely ABC-PCNN,is proposed here. It combines a new criterion of product cross entropy with the standard simplified PCNN model. The product cross entropy is used as the fitness function to optimize the connection output coefficient and threshold value by the ABC algorithm. Lena image and blood cell image are used to evaluate the PCNN model and the ABC-PCNN model respectively. The experimental results show that the adaptive image segmentation by the ABC-PCNN model outperforms that by the PCNN model. As the overlapping areas need secondary segmentation in the segmented blood cell image,corners and center coordinates are used to locate the dividing line and to get the＆amp;nbsp;final image segmentation. The method proposed here is effective and can obtain better segmentation results.