880 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 22, NO. 6, JUNE 2011 A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation Yuli Chen, Sung-Kee Park, Yide Ma, and Rajeshkanna Ala Abstract— An automatic parameter setting method of a simpli- fied pulse coupled neural network (SPCNN) is proposed here. Our method successfully determines all the adjustable parameters in SPCNN and does not need any training and trials as required by previous methods. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons, and then deduce the sub-intensity range expression of each segment based on the general formulae. Besides, we extract information from an input image, such as the standard deviation and the optimal histogram threshold of the image, and attempt to build a direct relation between the dynamic properties of neurons and the static properties of each input image. Finally, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset, rather than synthetic images, prove the validity and efficiency of our proposed automatic parameter setting method of SPCNN. Index Terms— Automatic parameter setting, dynamic property, general formulae, image segmentation, optimal histogram thresh- old, simplified pulse coupled neural network, standard deviation, static property, sub-intensity range. I. I NTRODUCTION E CHORN’S cortical model, a bio-inspired neural network, was developed in light of synchronous dynamics of neuronal activity in cat visual cortex [1]–[3]. According to its ability to cause the adjacent neurons with similar inputs to pulse synchronously, it was soon recognized as having significant potential in image processing. Therefore, pulse cou- pled neural network (PCNN) model, a tailored and modified version of Echorn’s cortical model, was developed by Johnson et al. and became more suitable for image processing [4]–[10]. Nowadays the standard PCNN model is usually simplified to achieve lower computational complexity while remaining the essential visual cortical property. The representative simplified Manuscript received March 20, 2010; revised March 2, 2011; accepted March 6, 2011. Date of publication May 5, 2011; date of current version June 2, 2011. This work was supported in part by the National Science Foundation of China under Grant 60872109 and the Korea Institute of Science and Technology. Y. Chen is with the School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China. She is also with the Center for Cognitive Robotics Research, Robotics/Systems Division, Korea Institute of Science and Technology, Seoul 136-791, Korea (Corresponding author e-mail: chenyuli2008@live.cn). S.-K. Park and R. Ala are with the Center for Cognitive Robotics Research, Robotics/Systems Division, Korea Institute of Science and Technology, Seoul 136-791, Korea (e-mail: skee@kist.re.kr; ala_rajeshkanna@yahoo.co.in). Y. Ma is with the School of Information Science and Engineering, Lanzhou University, Gansu 730000, China (e-mail: ydma@lzu.edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNN.2011.2128880 models include the intersecting cortical model [11]–[13], the unit-linking PCNN model [14], [15] and the spiking cortical model (SCM) [16]. The past decade has seen the rapid development of PCNN in many aspects within the image processing field, such as image segmentation [7], [17]–[24], image shadow removal [25], image understanding [26], feature extraction [5], [14], [27], [28], pattern recognition [29], [30] and object recognition [31]–[33]. More detailed applications of PCNN can be found from literatures of Lindblad and Kinser [13], Ma [34] and Wang [35]. Among the applications in image processing, PCNN has great potential for developing image segmentation algorithm. However, the quality of the segmentation results strongly depends on the appropriate values of PCNN parameters. Moreover, the appropriate values of the parameters could only be adjusted manually or estimated through a heavy training, which has become a serious problem and constrained the further development of PCNN. Many researchers have attempted to solve this problem. For example, Kuntimad and Ranganath [17] determined the mini- mum amplitude value of dynamic threshold which guarantees each neuron fires exactly once during a pulsing cycle, and ana- lyzed the minimum and maximum values of linking strength β for a perfect image segmentation after acquiring the intensity ranges of object and background. Based on this principle of linking strength β , Karvonen [18] computed a specific value of the linking strength β according to the distribution information of the segments obtained from training synthetic aperture radar images. Stewart et al. [19] proposed a region growing PCNN for multi-value image segmentation, but it requires to manually set the time-variant dynamic threshold E and linking strength β with incremental constants. The method introduced by Bi et al. [24] determined the weight matrixes and linking strength β adaptively according to spatial and gray characteristics, but they empirically set the amplitude and decrement constant of dynamic threshold E . In addition, Yonekawa et al. [23] automatically adjusted the linking strength β , synaptic weight W and exponential decay coefficient α e but it required many iterations of trials. So far, most of the previous methods could not set all the parameters of PCNN automatically, except the methods proposed by Berg et al. [21] and Ma et al. [22]. The former is to evolve PCNN neurons by automatic design of algorithms through evolution, while the latter is to build an automated PCNN system based on genetic algorithm [22]. However, these algorithms require repetition of trials and a heavy 1045–9227/$26.00 © 2011 IEEE