A fire-controlled MSPCNN and its applications for image processing.

• We design a fire-controlled MSPCNN with an automatic parameter setting method. Hereinto, the adaptive parameters α , β , V , and R n are defined to control the firing time of each neuron, and the predetermined parameter P is used for determining the total iteration times of all the neurons. • We propose a color image quantization method and a gallbladder image location method based on the FC-MSPCNN. Compared to other state-of-the-art algorithms, our proposed methods exhibit better image processing performances. A long-term research goal of pulse-coupled neural network (PCNN) is to control neuronal firing states at each iteration. Recently, we propose a fire-controlled MSPCNN model (FC-MSPCNN) and provide a parameter setting method to control firing and fired neurons within an effective pulse cycle. We firstly design the proposed model according to previous prevalent PCNN models. Secondly, the setting methods of the adaptive parameters α , β, V , and R n are given to control neuronal firing time more effectively. Thirdly, a predetermined parameter P will determine the total iteration times of all the neurons. Fourthly, we also propose a color image quantization method and a gallbladder image location method based on the FC-MSPCNN. The evaluation experiments achieve good image processing performances compared to prevalent PCNN models and prove the effectiveness and robustness of the proposed methods. [ABSTRACT FROM AUTHOR]