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Image segmentation based on ABC-PCNN model


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Image segmentation based on ABC-PCNN model
Liao Chuanzhu1Zhang Dan2Jiang Mingyan3
1.Department of Electronic Engineering of Zhangzhou Institute of Technology,Zhangzhou 363000,China; 2.School of Information Science and Engineering,Shandong University,Jinan 250100,China
pulse coupled neural network artificial bee colony artificial bee colony-pulse coupled neural network product cross entropy image segmentation
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 final image segmentation.The method proposed here is effective and can obtain better segmentation results.


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Last Update: 2014-08-31